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Masters Degrees (Computational Sciences)

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Graduate education in Computational Science and Engineering (CMSE) at Koç University is offered through an interdisciplinary program among the Departments of the College of Arts and Sciences and the College of Engineering. Read more
Graduate education in Computational Science and Engineering (CMSE) at Koç University is offered through an interdisciplinary program among the Departments of the College of Arts and Sciences and the College of Engineering. In this program graduate students are trained on modern computational science techniques and their applications to solve scientific and engineering problems. New technological problems and associated research challenges heavily depend on computational modeling and problem solving. Because of the availability of powerful and inexpensive computers model-based computational experimentation is now a standard approach to analysis and design of complex systems where real experiments can be expensive or infeasible. Graduates of the CMSE Program should be capable of formulating solutions to computational problems through the use of multidisciplinary knowledge gained from a combination of classroom and laboratory experiences in basic sciences and engineering. Individuals with B.S. degrees in biology, chemistry, physics, and related engineering disciplines should apply for graduate study in the CMSE Program.

Current faculty projects and research interests:

• Computational Biology & Bioinformatics
• Computational Chemistry
• Computational Physics
• Molecular Dynamics and Simulation
• Parallel and High Performance Computing
• Computational Fluid Dynamics
• Dynamical and Stochastic Systems
• Quantum Mechanics of Many Body Systems
• Electronic Design Automation
• Numerical Methods
• Simulation of Material Synthesis
• Structural Dynamics
• Biomedical Modeling and Simulation
• Virtual Environments

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Penn’s Master of Chemical Sciences is designed for your success. Chemistry professionals are at the forefront of the human quest to solve ever-evolving challenges in agriculture, healthcare and the environment. Read more
Penn’s Master of Chemical Sciences is designed for your success
Chemistry professionals are at the forefront of the human quest to solve ever-evolving challenges in agriculture, healthcare and the environment. As new discoveries are made, so are new industries — and new opportunities. Whether you’re currently a chemistry professional or seeking to enter the field, Penn’s rigorous Master of Chemical Sciences (MCS) builds on your level of expertise to prepare you to take advantage of the myriad career possibilities available in the chemical sciences. With a faculty of leading academic researchers and experienced industry consultants, we provide the academic and professional opportunities you need to achieve your unique goals.

The Penn Master of Chemical Sciences connects you with the resources of an Ivy League institution and provides you with theoretical and technical expertise in biological chemistry, inorganic chemistry, organic chemistry, physical chemistry, environmental chemistry and materials. In our various seminar series, you will also regularly hear from chemistry professionals who work in a variety of research and applied settings, allowing you to consider new paths and how best to take advantage of the program itself to prepare for your ideal career.

Preparation for professional success
If you’ve recently graduated from college and have a strong background in chemistry, the Master of Chemical Sciences offers you a exceptional preparation to enter a chemistry profession. In our program, you will gain the skills and confidence to become a competitive candidate for potential employers as you discover and pursue your individual interests within the field of chemistry. Our faculty members bring a wealth of research expertise and industry knowledge to help you define your career direction.

For working professionals in the chemical or pharmaceutical industries, the Master of Chemical Sciences accelerates your career by expanding and refreshing your expertise and enhancing your research experiences. We provide full- and part-time options so you can pursue your education without interrupting your career. You can complete the 10-course program in one and a half to four years, depending on course load.

The culminating element of our curriculum, the capstone project, both tests and defines your program mastery. During the capstone exercise, you will propose and defend a complex project of your choice, that allows you to stake out a new professional niche and demonstrate your abilities to current or prospective employers.

Graduates will pursue fulfilling careers in a variety of cutting-edge jobs across government, education and corporate sectors. As part of the Penn Alumni network, you’ll join a group of professionals that spans the globe and expands your professional horizons.

Courses and Curriculum

The Master of Chemical Sciences degree is designed to give you a well-rounded, mechanistic foundation in a blend of chemistry topics. To that end, the curriculum is structured with a combination of core concentration courses and electives, which allow you to focus on topics best suited to your interests and goals.

As a new student in the Master of Chemical Sciences program, you will meet with your academic advisor to review your previous experiences and your future goals. Based on this discussion, you will create an individualized academic schedule.

The Master of Chemical Sciences requires the minimum completion of 10 course units (c.u.)* as follows:

Pro-Seminar (1 c.u.)
Core concentration courses (4-6 c.u., depending on concentration and advisor recommendations)
Elective courses in Chemistry, such as computational chemistry, environmental chemistry, medicinal chemistry, catalysis and energy (2-4 c.u., depending on concentration and advisor recommendations)
Optional Independent Studies (1 c.u.)
Capstone project (1 c.u.)
Pro-Seminar course (CHEM 599: 1 c.u.)
The Pro-Seminar will review fundamental concepts regarding research design, the scientific method and professional scientific communication. The course will also familiarize students with techniques for searching scientific databases and with the basis of ethical conduct in science.

Concentration courses
The concentration courses allow you to develop specific expertise and also signify your mastery of a field to potential employers.

The number of elective courses you take will depend upon the requirements for your area of concentration, and upon the curriculum that you plan with your academic advisor. These concentration courses allow you to acquire the skills and the critical perspective necessary to master a chemical sciences subdiscipline, and will help prepare you to pursue the final capstone project (below).

You may choose from the following six chemical sciences concentrations:

Biological Chemistry
Inorganic Chemistry
Organic Chemistry
Physical Chemistry
Environmental Chemistry
Materials
Independent Studies
The optional Independent Studies course will be offered each fall and spring semester, giving you an opportunity to participate in one of the research projects being conducted in one of our chemistry laboratories. During the study, you will also learn analytical skills relevant to your capstone research project and career goals. You can participate in the Independent Studies course during your first year in the program as a one-course unit elective course option. (CHEM 910: 1 c.u. maximum)

Capstone project (1 c.u.)

The capstone project is a distinguishing feature of the Master of Chemical Sciences program, blending academic and professional experiences and serving as the culmination of your work in the program. You will develop a project drawing from your learning in and outside of the classroom to demonstrate mastery of an area in the chemical sciences.

The subject of this project is related to your professional concentration and may be selected to complement or further develop a work-related interest. It's an opportunity to showcase your specialization and your unique perspective within the field.

Your capstone component may be a Penn laboratory research project, an off-campus laboratory research project or a literature-based review project. All components will require a completed scientific report. It is expected that the capstone project will take an average of six months to complete. Most students are expected to start at the end of the first academic year in the summer and conclude at the end of fall semester of the second year. Depending on the capstone option selected, students may begin to work on the capstone as early as the spring semester of their first year in the program.

All capstone project proposals must be pre-approved by your concentration advisor, Master of Chemical Sciences Program Director and if applicable, your off-campus project supervisor. If necessary, nondisclosure agreements will be signed by students securing projects with private companies. Additionally, students from private industry may be able to complete a defined capstone project at their current place of employment. All capstone projects culminate in a final written report, to be graded by the student's concentration advisor who is a member of the standing faculty or staff instructor in the Chemistry Department.

*Academic credit is defined by the University of Pennsylvania as a course unit (c.u.). Generally, a 1 c.u. course at Penn is equivalent to a three or four semester hour course elsewhere. In general, the average course offered at Penn is listed as being worth 1 c.u.; courses that include a lecture and a lab are often worth 1.5 c.u.

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Research profile. The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. Read more

Research profile

The Institute for Adaptive and Neural Computation (IANC) is a world-leading institute dedicated to the theoretical and empirical study of adaptive processes in both artificial and biological systems. We are one of the UK’s largest and most prestigious academic teams in these fields.

We foster world-class interdisciplinary and collaborative research bringing together a range of disciplines.

Our research falls into three areas:

  • machine learning
  • computational neuroscience
  • computational biology

In machine learning we develop probabilistic methods that find patterns and structure in data, and apply them to scientific and technological problems. Applications include areas as diverse as astronomy, health sciences and computing.

In computational neuroscience and neuroinformatics we study how the brain processes information, and analyse and interpret data from neuroscientific experiments

The focus in the computational biology area is to develop computational strategies to store, analyse and model a variety of biological data (from protein measurements to insect behavioural data).

Training and support

You carry out your research within a research group under the guidance of a supervisor. You will be expected to attend seminars and meetings of relevant research groups and may also attend lectures that are relevant to your research topic. Periodic reviews of your progress will be conducted to assist with research planning.

A programme of transferable skills courses facilitates broader professional development in a wide range of topics, from writing and presentation skills to entrepreneurship and career strategies.

The School of Informatics holds a Silver Athena SWAN award, in recognition of our commitment to advance the representation of women in science, mathematics, engineering and technology. The School is deploying a range of strategies to help female staff and students of all stages in their careers and we seek regular feedback from our research community on our performance.

Facilities

The award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.

It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.

Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.

Among our entrepreneurial initiatives is Informatics Ventures, set up in 2008 to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.

Career opportunities

The research you will undertake at IANC is perfectly suited to a career in academia, where you’ll be able to use your knowledge to advance this important field. Some graduates take their skills into commercial research posts, and find success in creating systems that can be used in everyday applications.



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What is the Erasmus Mundus Master of Science in Theoretical Chemistry and Computational Modelling all about?. Get in at the bleeding edge of contemporary chemistry. Read more

What is the Erasmus Mundus Master of Science in Theoretical Chemistry and Computational Modelling all about?

Get in at the bleeding edge of contemporary chemistry: theoretical and computational chemistry are marking the new era that lies ahead in the molecular sciences. The aim of the programme is to train scientists that are able to address a wide range of problems inmodern chemical, physical and biological sciences through the combination of theoretical and computational tools.

This programme is organised by:

  • Universidad Autónoma de Madrid (coordinating institution), Spain
  • Universiteit Groningen, the Netherlands
  • KU Leuven, Belgium
  • Università degli Studi di Perugia, Italy
  • Universidade do Porto, Portugal
  • Université Paul Sabatier - Toulouse III, France
  • Universitat de Valencia, Spain

The Erasmus Mundus Master of Theoretical Chemistry and Computational Modelling is a joint initiative of these European Universities, including KU Leuven and co-ordinated by the Universidad Autónoma de Madrid. 

This is an initial Master's programme and can be followed on a full-time or part-time basis.

Structure

The programme is organised according to a two-year structure.

  • The first year of the programme introduces you to concepts and methods. The core of the programme is an intensive international course intended to bring all participants to a common level of excellence. It takes place in the summer between year 1 and year 2 and runs for four weeks. Coursework is taught by a select group of invited international experts.
  • The second year of the programme is devoted to tutorials covering the material dealt with in the intensive course and to a thesis project carried out in part at another university within the consortium. The intensive course is organised at the partner institutions on a rotating basis.

Department

The Department of Chemistry consists of four divisions, all of which conduct highquality research embedded in well-established collaborations with other universities, research institutes and companies around the world. Its academic staff is committed to excellence in teaching and research. Although the department's primary goal is to obtain insight into the composition, structure and properties of chemical compounds and the design, synthesis and development of new (bio)molecular materials, this knowledge often leads to applications with important economic or societal benefits.

The department aims to develop and maintain leading, internationally renowned research programmes dedicated to solving fundamental and applied problems in the fields of:

  • the design, synthesis and characterisation of new compounds (organic-inorganic, polymers).
  • the simulation of the properties and reactivity of (bio)molecules, polymers and clusters by quantum chemical and molecular modelling methods.
  • the determination of the chemical and physical properties of (bio)molecules, and polymers on the molecular as well as on the material level by spectroscopy, microscopy and other characterisation tools as related to their structure.

Objectives

Modern Chemistry is unthinkable without the achievements of Theoretical and Computational Chemistry. As a result these disciplines have become a mandatory tool for the molecular science towards the end of the 20th century, and they will undoubtedly mark the new era that lies ahead of us.

In this perspective the training and formation of the new generations of computational and theoretical chemists with a deep and broad knowledge is of paramount importance. Experts from seven European universities have decided to join forces in a European Master Course for Theoretical Chemistry and Computational Modelling (TCCM). This course is recognized as an Erasmus Mundus course by the European Union.

Graduates will have acquired the skills and competences for advanced research in chemical, physical and material sciences, will be qualified to collaborate in an international research team, and will be able to develop professional activities as experts in molecular design in pharmaceutical industry, petrochemical companies and new-materials industry.

Career perspectives

In addition to commanding sound theoretical knowledge in chemistry and computational modelling, you will be equipped to apply any of the scientific codes mastered in the programme in a work environment, or develop new codes to address new requirements associated with research or productive activities.

You will have attained the necessary skills to pursue a scientific career as a doctoral student in chemistry, physics or material science. You will also be qualified to work as an expert in molecular design in the pharmaceutical industry, at petrochemical companies and in the new-materials industry. You will also have a suitable profile to work as a computational expert.



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What are the laws of nature governing the universe from elementary particles to the formation and evolution of the solar system, stars, and galaxies? In the Master’s Programme in Particle Physics and Astrophysical Sciences, you will focus on gaining a quantitative understanding of these phenomena. Read more
What are the laws of nature governing the universe from elementary particles to the formation and evolution of the solar system, stars, and galaxies? In the Master’s Programme in Particle Physics and Astrophysical Sciences, you will focus on gaining a quantitative understanding of these phenomena.

With the expertise in basic research that you will gain in the programme, you can pursue a career in research. You will also acquire proficiency in the use of mathematical methods, IT tools and/or experimental equipment, as well as strong problem-solving and logical deduction skills. These will qualify you for a wide range of positions in the private sector.

After completing the programme, you will:
-Have wide-ranging knowledge of particle physics and/or astrophysical phenomena.
-Have good analytical, deductive and computational skills.
-Be able to apply theoretical, computational and/or experimental methods to the analysis and understanding of various phenomena.
-Be able to generalize your knowledge of particle physics and astrophysical phenomena as well as identify their interconnections.
-Be able to formulate hypotheses and test them based your knowledge.

The teaching in particle physics and astrophysical sciences is largely based on the basic research. Basic research conducted at the University of Helsinki has received top ratings in international university rankings. The in-depth learning offered by international research groups will form a solid foundation for your lifelong learning.

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

The understanding of the microscopic structure of matter, astronomical phenomena and the dynamics of the universe is at the forefront of basic research today. The advancement of such research in the future will require increasingly sophisticated theoretical, computational and experimental methods.

The study track in elementary particle physics and cosmology focuses on experimental or theoretical particle physics or cosmology. The theories that form our current understanding of these issues must be continuously re-evaluated in the light of new experimental results. In addition to analytical computation skills, this requires thorough mastery of numerical analysis methods. In experimental particle physics, the main challenges pertain to the management and processing of continuously increasing amount of data.

The study track in astrophysical sciences focuses on observational or theoretical astronomy or space physics. Our understanding of space, ranging from near Earth space all the way to structure of the universe, is being continuously redefined because of improved experimental equipment located both in space and on the Earth’s surface. Several probes are also carrying out direct measurements of planets, moons and interplanetary plasma in our solar system. Another key discipline is theoretical astrophysics which, with the help of increasingly efficient supercomputers, enables us to create in-depth models of various phenomena in the universe in general and the field of space physics in particular. Finally, plasma physics is an important tool in both space physics and astronomy research.

Selection of the Major

The Master’s programme includes two study tracks:
-Particle physics and cosmology
-Astrophysical sciences

Courses in the programme have been compiled into modules. Both study tracks contain a mandatory core module that includes a research seminar. The study tracks are divided into specialisations that focus on astronomy, space physics, particle physics or cosmology. Courses typically include lectures, exercises, group work and research literature and end in examinations and/or final assignments. In addition, some studies can be completed as book examinations.

Programme Structure

The scope of the Master’s programme is 120 credits (ECTS), which can be completed in two years. The degree consists of:
-90 credits of Master’s studies, including a Master’s thesis (30 credits).
-30 credits of other studies from the Master’s programme or other degree programmes.

In addition, your studies include a personal study plan as well as career orientation and planning. You might also take part in a traineeship, elective studies offered by the Master’s Programme in Particle Physics and Astrophysical Sciences, or studies offered by other degree programmes.

Career Prospects

A Master’s degree in elementary particle physics or astrophysical sciences provides you with excellent qualifications for postgraduate education in research or for a career in diverse positions both in Finland and abroad. As a Master’s graduate you could begin a career in research and development in industry as well as in universities and other research institutes that enable you to conduct independent research on a topic that interests you.

Potential employers and career opportunities include:
-Research institutes in Finland and abroad (basic scientific research).
-Universities and universities of applied sciences (teaching).
-Industry, particularly high technology companies (applied research and development, managerial duties).
-Software production, e.g., the game sector.
-Diverse planning and consulting positions.

Master’s graduates from equivalent study tracks under the previous degree system have embarked on careers in:
-Research and teaching positions in Finnish universities and research institutes.
-Research and teaching positions abroad, for example at CERN (the European Organization for Nuclear Research), ESA (the European Space Agency), ESO (the European Southern Observatory), and NASA (the National Aeronautics and Space Administration).
-Administrative positions, for example at the Academy of Finland or the Finnish Funding Agency for Innovation (Tekes).
-The business sector.

The strong theoretical and analytical skills you will acquire in the programme are in great demand in fields such as:
-Data analysis (industry, media companies, game companies, financing).
-Industrial research, development and consulting (at, e.g., Nokia, Ericsson, Apple, Sanoma, Spinverse, Supercell, Nielsen, Valo -Research and Trading, Planmeca, Reaktor, Comptel, and Goldman Sachs).

Internationalization

Our multilingual Master’s programme is highly international. The Department hosts a large number of international students and staff members. In addition, the University of Helsinki and the Faculty of Science provide many opportunities for international engagement:
-Student exchange at one of the destinations available through the Faculty or the University.
-International traineeships.
-English-language teaching offered by the Faculty.
-Master’s thesis project as a member of one of the international research groups operating under the programme.
-Cooperation with international students enrolled in the programme.
-International duties in subject-specific student organisations or the Student Union of the University of Helsinki.
-Language courses organised by the Language Centre of the University of Helsinki.

The Faculty of Science is a top research institute in its fields among European universities. Its partners include many leading international research institutes, such as the European Organization for Nuclear Research (CERN), the European Space Agency (ESA) and the European Southern Observatory (ESO).

As a student at the Faculty of Science, you will have the opportunity to complete a research traineeship period at, for example, CERN in Geneva. By completing a traineeship at one of the internationally active research groups on campus you will be able to acquaint yourself and network with the international scientific community during your Master’s studies. The international student exchange programmes available at the University provide numerous opportunities to complete part of your degree at a university abroad.

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Introduced in 2004, this course was developed by the Cambridge Computational Biology Institute, an interdisciplinary centre bringing together the unique strengths of Cambridge in medicine, biology, mathematics and the physical sciences. Read more
Introduced in 2004, this course was developed by the Cambridge Computational Biology Institute, an interdisciplinary centre bringing together the unique strengths of Cambridge in medicine, biology, mathematics and the physical sciences.

The course is aimed at introducing students to quantitative aspects of biological and medical sciences. It is intended for mathematicians, computer scientists and others wishing to learn about the subject in preparation for a PhD course or a career in industry. It is also suitable for students with a first degree in biosciences as long as they have strong quantitative skills (which should be documented in the application).

This 11-month course consists of core modules in bioinformatics, scientific programming with R, genomics, systems biology and network biology. Before the start of the first term, students are required to attend an introductory course in molecular biology. Courses are delivered in association with several University departments from the Schools of Biological Sciences and Physical Sciences, groups within the School of Clinical Medicine, the European Bioinformatics Institute and the Sanger Institute. The course concludes with a three-month internship in a university or industrial laboratory.

Visit the website: http://www.graduate.study.cam.ac.uk/courses/directory/maammpcbi

Learning Outcomes

After completing the MPhil in Computational Biology, students will be expected to have:

- acquired a sound knowledge of a range of tools and methods in computational biology;
- developed the capacity for independent study and problem solving at a higher level;
- undertaken an internship project within a laboratory or group environment, and produced a project report;
- given at least one presentation on their project.

Format

The course combines taught lectures (October-April), followed by a summer internship project (May-August). There are typically 3-4 taught modules per term, and each module consists of 16 hours of lectures. Each module is assessed by coursework, and there is one general examination in May.

The Course Director is available throughout the year for individual meetings, and briefly meets termly with each student to check on progress. Each lecturer is also encouraged to arrange an office hour whereby students can talk about their progress.

Lectures: Typically 16 hours per module, with students taking 8 modules.

Journal Clubs: A weekly seminar is held during the first two terms on topics across Computational Biology. These seminars help students to select an appropriate project.

Placements

Students undertake a mandatory internship (May to August) in either a university or industrial laboratory. The Department will compile a list of possible opportunities which students can discuss directly with the host laboratory. Alternatively students may organise their own internship, subject to the approval of the Course Director.

Assessment

A 18,000 word (maximum) report must be written to summarise the student's internship. An oral presentation on this report must also be given.

Students give a 25 minute presentation on their project as part of the formal assessment. Some assessed coursework may also require students to present their work.

Each module is assessed typically by two written assignments. These assignments involve significant computational elements.

A compulsory two-hour general examination is sat in May.

Continuing

MPhil students wishing to apply for a PhD at Cambridge must apply via the Graduate Admissions Office for continuation by the relevant deadline.

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

General Funding Opportunities: http://www.2016.graduate.study.cam.ac.uk/finance/funding

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Recent years breakthrough discoveries in health sciences have generally been achieved by effective cooperation between interdisciplinary research teams, which included members from medicine, basic sciences and engineering. Read more
Recent years breakthrough discoveries in health sciences have generally been achieved by effective cooperation between interdisciplinary research teams, which included members from medicine, basic sciences and engineering. Such a cooperation provides a broad visionary approach and strong scientific basis for a better understanding of the health related problems and allows the development of novel technologies to improve the quality of life.
Koç University Biomedical Sciences and Engineering (BMSE) MS and PhD programs have been developed with this philosophy in mind and offer unique, truly interdisciplinary graduate education and leading edge research opportunities for students with different disciplines, which include basic sciences (chemistry, physics and biological sciences) engineering (chemical, mechanical and electrical engineering), medicine and related health sciences programs and provide them with the vision, knowledge and tools to become the future leaders.

Current faculty projects and research interests:

• Computational and Quantative Biology
• Biometric Materials and Islet Cell Bioengineering
• Robıtics and Mechanics
• Computational Biology and Bioinformatics
• Molecular biochemistry
• Computational Systems
• Biofluids and Cardiovascular Mechanics
• Polymer Science and Technology
• Mitochondrial Biogenesis
• Cell Biology
• Microphotonics
• Optofluidic and Nano-Optics

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Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science. Read more
Our highly sought-after graduates benefit from a programme that integrates training in identifying, framing and effectively researching social problems with a leading computational approach to social science.

Furthermore, we are home to the Centre for Research in Social Simulation (CRESS) and its world-leading expertise in agent-based modelling.

PROGRAMME OVERVIEW

Interest in simulation has grown rapidly in the social sciences. New methods have been developed to tackle this complexity. This programme will integrate traditional and new methods, to model complexity, evolution and the adaptation of social systems.

These new methods are having an increasing influence on policy research through a growing recognition that many social problems are insufficiently served by traditional policy modelling approaches.

The Masters in Social Science and Complexity will equip you to develop expertise in the methods necessary to tackle complex, policy-relevant, real-world social problems through a combination of traditional and computational social science methods, and with a particular focus on policy relevance.

PROGRAMME STRUCTURE

This programme is studied full-time over one academic year and part-time over two academic years. It consists of eight taught modules and a dissertation. The following modules are indicative, reflecting the information available at the time of publication. Please note that not all modules described are compulsory and may be subject to teaching availability and/or student demand.
-Data Analysis
-Field Methods
-Computational Modelling
-Theory Model Data
-Modelling the Complex World
-Policy Modelling
-Theory and Method
-Statistical Modelling
-Evaluation Research
-Dissertation

EDUCATIONAL AIMS OF THE PROGRAMME

The main aims of the programme are to:
-Provide an appropriate training for students preparing MPhil/PhD theses, or for 
 students going on to employment involving the use of social science and policy research
-Provide training that fully integrates social science, policy modelling and computational methodologies to a high standard
-Provide training resulting in students with high quality analytic, methodological, computational and communication skills

PROGRAMME LEARNING OUTCOMES
The programme provides opportunities for students to develop and demonstrate knowledge and understanding, skills, qualities and other attributes in the following areas:
-Develop skills in tackling real world policy problems with creativity and sound methodological judgment
-Cover the principles of research design and strategy, including formulating research 
questions or hypotheses and translating these into practicable research designs and models
-Introduce students to the methodological and epistemological issues surrounding research in the social sciences in general and computational modelling in particular
-Develop skills in programming in NetLogo for the implementation of agent-based models for the modelling of social phenomena
-Develop skills in the acquisition and analysis of social science data
-Make students aware of the range of secondary data available and equip them to evaluate its utility for their research
-Develop skills in searching for and retrieving information, using library and Internet resources
-Develop skills in the use of SPSS, and in the main statistical techniques of data analysis, including multivariate analysis
-Develop skills in the use of CAQDAS software for the analysis of qualitative data
-Develop skills in writing, in the preparation of a research proposal, in the presentation ofresearch results and in verbal communication
-Help students to prepare their research results for wider dissemination, in the form of seminar papers, conference presentations, reports and publications, in a form suitable for a range of audiences, including academics, stakeholders, policy makers, professionals, service users and the general public

Knowledge and understanding
-Show advanced knowledge of qualitative, quantitative and computational methodologies in the social science
-Show advanced knowledge of modelling methodologies, model construction and analysis
-Show critical understanding of methodological and epistemological challenges of social science and computer modelling
-Show critical awareness and understanding of the methodological implications of a range of sociological theories and approaches
-Show understanding the use and value of a wide range of different research approaches across the quantitative and qualitative spectra
-Show advanced knowledge in data collection, analysis and data driven modelling
-Show advanced knowledge of policy relevant social science research and modelling
-Show advanced understanding of the policy process and the role of social science and modelling therein
-Show advanced knowledge of statistical modelling

Intellectual / cognitive skills
-Systematically formulate researchable problems; analyse and conceptualise issues; critically appreciate alternative approaches to research; report to a range of audiences
-Conceptual development of Social Science and Complexity models to creatively enhance the understanding of social phenomena
-Integration of qualitative, quantitative and computational data
-Judgement of problem-methodology match
-Analyse qualitative and quantitative data drawn both from ‘real world’ and ‘virtual world’ environments, using basic and more advanced techniques, and draw warranted conclusions
-Develop original insights, questions, analyses and interpretations in respect of research questions
-Critically evaluate the range of approaches to research

Professional practical skills
-Formulate, design, plan, carry out and report on a complete research project
-Use the range of traditional and computational techniques employed in sociological research
-Ability to produce well founded, data driven and validated computational models
-Generate both quantitative and qualitative data through an array of techniques, and select techniques of data generation on appropriate methodological bases
-Employ a quantitative (SPSS) and qualitative software package to manage and analyse data
-Plan, manage and execute research as part of a team and as a sole researcher
-Ability to communicate research findings models in social science and policy relevant ways
-Ability to manage independent research

Key / transferable skills
-Communicate complex ideas, principles and theories by oral, written and visual means
-Apply computational modelling methodology to complex social issues in appropriate ways
-Creativity in approaching complex problems and a the ability of communicating and justifying problem solutions
-Apply computing skills for computational modelling, research instrument design, data analysis, and report writing and presentation
-Work to deadlines and within work schedules
-Work independently or as part of a team
-Demonstrate experience of a work environment

PLACEMENTS

On the MSc Social Science and Complexity, we offer the opportunity to take a research placement during the Easter vacation. This will provide you with first-hand experience of real-life policy research in action.

Organisations in which placements might be possible are a number of consultancies (e.g. Sandtable), government departments (e.g. Defra) and academic research centres (e.g. Centre for Policy Modelling at Manchester).

CAREER OPPORTUNITIES

Computational methods and especially computer-based simulations, are becoming increasingly important in academic social science and policy making.

Graduates might find career opportunities in government departments, consultancies, government departments, consultancies, NGOs and academia.

GLOBAL OPPORTUNITIES

We often give our students the opportunity to acquire international experience during their degrees by taking advantage of our exchange agreements with overseas universities.

In addition to the hugely enjoyable and satisfying experience, time spent abroad adds a distinctive element to your CV.

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The basis of natural sciences is the modelling of phenomena and solving these models. The Master’s programme in theoretical and computational methods will give you a strong basis in the theoretical methods, modelling, and mathematical and numerical analysis within physics, mathematics, chemistry and/or computer science. Read more
The basis of natural sciences is the modelling of phenomena and solving these models. The Master’s programme in theoretical and computational methods will give you a strong basis in the theoretical methods, modelling, and mathematical and numerical analysis within physics, mathematics, chemistry and/or computer science. The special feature of this programme is that you can combine the above disciplines into a comprehensive programme. It is well suited for the needs of basic research and for many fields of application. This programme requires a strong commitment from you to develop your own skills and plan your degree. You can tailor your programme according to your existing knowledge and interests, in cooperation with the programme professors.

The programme’s strong scientific emphasis makes it a natural gateway to further studies in physics, mathematics, chemistry, and computer science. This will usually take place within one of the research groups working on the Kumpula campus.

Upon completing the Master’s programme, you will:
-Have a solid basis of skills in your chosen scientific field.
-Have good skills in analytical and computational thinking and deduction.
-Be able to apply theoretical and computational methods to the analysis and understanding of problems in various fields.
-Be able to generalise information on scientific phenomena, and identify the inner relationships.
-Be able to create mathematical models of natural phenomena.
-Be able to solve the models, both analytically and numerically.

As a graduate of this Master’s programme you can work as an expert in many kinds of scientific jobs in the private and the public sectors. The employment rate in this field is good.

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees: https://studyinfo.fi/wp2/en/higher-education/higher-education-institutions-will-introduce-tuition-fees-in-autumn-2017/am-i-required-to-pay-tuition-fees/

Programme Contents

The special feature of this programme is its great scope: it consists of several modules in physics, mathematics, chemistry, and/or computer science. Out of these, you may select a suitable group of subjects according to your interests and the courses you took for your Bachelor's degree. The programme incorporates modules from e.g. the following areas:
-Theoretical physics
-Mathematics
-Cosmology and particle physics
-Computational physics
-Physical chemistry
-Laser spectroscopy
-Mathematical physics and stochastics
-Applied analysis
-Software engineering
-Theoretical computer science

The courses include group and lecture instruction, exercises, literature, and workshops. Most courses also include exams or project assignments. In addition, you can complete some courses independently, by taking exams.

Selection of the Major

This Master’s programme does not have any sub-programmes; instead, can can tailor a suitable combination according to your plans and existing knowledge from the modules in physics, mathematics, chemistry, and computer science. Your personal study plan will ensure that your courses will form a functional combination.

Programme Structure

The Master’s programme comprises 120 credits (ECTS) and it is possible to complete the degree in two academic years. The degree includes:
-90 credits of courses in the Master’s programme, including the Master’s thesis (Pro gradu) of 30 credits.
-30 credits of other courses from your Master’s programme or other programmes.

Your studies will include a personal study plan, working-life orientation, and career planning. The other studies could also include a traineeship, complementary courses in your major or minor subject, or a completely new minor subject.

Career Prospects

The Master’s degree in sciences applying theoretical and computational methods gives you an excellent basis for postgraduate studies or for work in many careers in Finland or internationally. Masters of Science employed within research and R&D in industry are very well paid. On the other hand, a career at the university or a research institute lets you carry out academic research on a topic of your own choosing.

As a graduate with an MSc degree you could embark on a career in:
-Industry, especially advanced technology corporations (applied research and R&D, leadership).
-Universities and research institutes abroad and in Finland (basic scientific research).
-Teaching in universities and universities of applied sciences.
-Software engineering, e.g. gaming industry.
-Various design and consultation jobs in the public and private sectors.

Graduates of similar programmes in the earlier degree system have found employment as researchers and teachers in universities and research institutes in Finland and abroad (e.g. CERN, ESA, NASA), for example, in administration (e.g. the Finnish Academy), and in private corporations. The strong analytical skills provided by the education are sought after in areas such as data analysis (industries, media companies, gaming industry, finance), and corporate research, product development, and consultation (e.g. Nokia, Ericsson, Apple, Sanoma, Spinverse, Supercell, Nielsen, Valo Research and Trading, Planmeca, Reaktor, Comptel, Vaisala, KaVo Kerr Group, IndoorAtlas and Goldman Sachs).

Internationalization

The Master’s programme works in a very international atmosphere, with many top researchers from Finland and abroad teaching in it. If you write your MSc thesis in one of the research groups, you will get first-hand experience of work in an international research project. In addition, the University of Helsinki and the Faculty of Science offer you many opportunities for international activities:
-Student exchange in one of the exchange locations of the faculty or university.
-Traineeships abroad.
-Courses given in English within the faculty.
-Cooperation with students in the international programme.
-International tasks within the students’ organisations or union.
-Language courses at the Language Centre of the University of Helsinki.

The Faculty of Science aims to be at the cutting edge of European research within its disciplines.

The collaboration partners include several top international research centres, such as CERN, ESA, ESRF, and ITER.R.

As a graduate student at the Faculty of Science, you will be able to apply for research training at places such as CERN in Geneva, Switzerland, or the ESRF centre in Grenoble, France. A traineeship in one of the internationally active research groups on campus will enable you to acquaint yourself and form contacts with the international research community during your studies. In addition, the international exchange programmes offer many opportunities for you to complete part of your degree at a foreign university.

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Introduced in 2004, this course was developed by the Cambridge Computational Biology Institute, an interdisciplinary centre bringing together the unique strengths of Cambridge in medicine, biology, mathematics and the physical sciences. Read more
Introduced in 2004, this course was developed by the Cambridge Computational Biology Institute, an interdisciplinary centre bringing together the unique strengths of Cambridge in medicine, biology, mathematics and the physical sciences.

The course is aimed at introducing students to quantitative aspects of biological and medical sciences. It is intended for mathematicians, computer scientists and others wishing to learn about the subject in preparation for a PhD course or a career in industry. It is also suitable for students with a first degree in biosciences as long as they have strong quantitative skills (which should be documented in the application).

This 11-month course consists of core modules in bioinformatics, scientific programming with R, genomics, systems biology and network biology. Before the start of the first term, students are required to attend an introductory course in molecular biology. Courses are delivered in association with several University departments from the Schools of Biological Sciences and Physical Sciences, groups within the School of Clinical Medicine, the European Bioinformatics Institute and the Sanger Institute. The course concludes with a three-month internship in a university or industrial laboratory.

See the website http://www.graduate.study.cam.ac.uk/courses/directory/maammpcbi

Course detail

After completing the MPhil in Computational Biology, students will be expected to have:

- acquired a sound knowledge of a range of tools and methods in computational biology;
- developed the capacity for independent study and problem solving at a higher level;
- undertaken an internship project within a laboratory or group environment, and produced a project report;
- given at least one presentation on their project.

Format

The course combines taught lectures (October-April), followed by a summer internship project (May-August). There are typically 3-4 taught modules per term, and each module consists of 16 hours of lectures. Each module is assessed by coursework, and there is one general examination in May.

Placements

Students undertake a mandatory internship (May to August) in either a university or industrial laboratory. The Department will compile a list of possible opportunities which students can discuss directly with the host laboratory. Alternatively students may organise their own internship, subject to the approval of the Course Director.

Assessment

A 18,000 word (maximum) report must be written to summarise the student's internship. An oral presentation on this report must also be given.

Each module is assessed typically by two written assignments. These assignments involve significant computational elements.

A compulsory two-hour general examination is sat in May.

Continuing

MPhil students wishing to apply for a PhD at Cambridge must apply via the Graduate Admissions Office for continuation by the relevant deadline.

How to apply: http://www.graduate.study.cam.ac.uk/applying

Funding Opportunities

There are no specific funding opportunities advertised for this course. For information on more general funding opportunities, please follow the link below.

General Funding Opportunities http://www.graduate.study.cam.ac.uk/finance/funding

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Cognitive neuroscience relates cognitive and behavioural functions to the underlying brain systems. Computational neuroscience uses data to construct rigorous computational models of brain function. Read more

About the course

Cognitive neuroscience relates cognitive and behavioural functions to the underlying brain systems. Computational neuroscience uses data to construct rigorous computational models of brain function. Put them together and these new disciplines are the key to explaining the relationship between brain and behaviour.

You’ll develop a broad and critical understanding of these two fields, along with an appreciation of different approaches to understanding brain function. Your range of computational and analytical skills, and an ability to generate and test hypotheses, will give you an excellent foundation for further research.

The course takes students from both life sciences and the physical sciences and engineering. Appropriate training is given to ensure all students can master the required skills and complete the course successfully.

Where your masters can take you

You’ll develop the skills and knowledge for all sorts of careers. Many of our graduates continue to PhD level. Others work as research associates and assistant psychologists for employers such as universities and the NHS. Throughout your course, you’ll have frequent reviews with your tutor to discuss your learning needs and objectives.

Applying psychology in the real world

Our ongoing collaborative projects with hospitals, mental health care units, the police and prison service, and several leading firms in business and industry will show you how psychology can be applied in the real world.

You’ll also benefit from our research excellence. We don’t just focus on one or two specialisms – with active researchers in most areas of psychology, we are consistently one of the highest-ranked research departments in the UK.

Our facilities

Whatever your particular interest, we have the facilities for your research. Our research environment was rated amongst the best in the country in the last national assessment. We are exceptionally well resourced for research in Social and Health Psychology, Clinical Psychology and Developmental Psychology, with a dedicated suite of rooms for different participant groups.

To give you the right tools for your research, there is a fully equipped neuroscience unit with excellent facilities for brain imaging, neuroanatomy, electrophysiology, behavioural neuroscience and computational neuroscience. We have access to a small-bore MRI device and to the University’s MRI facility for human studies.

Studentships and bursaries

Please contact us for the latest funding opportunities.

Core modules

Fundamentals of Cognitive Neuroscience; Fundamentals of Neuroscience; Computational Neuroscience 1: biologically grounded models; Mathematical Modelling and Research Skills; Computational Neuroscience 2: theoretical models; Brain Imaging and its Physical Foundations; Current Issues in Systems Neuroscience;Current Issues in Cognitive Neuroscience.

Teaching

Teaching is through lectures, seminars and laboratory classes.

Assessment

Examinations at the end of semesters one and two, written coursework and an extensive empirical research project over the summer.

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This course provides specialist skills in core systems biology with a focus on the development of computational and mathematical research skills. Read more
This course provides specialist skills in core systems biology with a focus on the development of computational and mathematical research skills. It specialises in computational design, providing essential computing and engineering skills that allow you to develop software to program biological systems.

This interdisciplinary course is based in the School of Computing Science and taught jointly with the Faculty of Medical Sciences and the School of Mathematics and Statistics. The course is ideal for students aiming for careers in industry or academia. We cater for students with a range of backgrounds, including Life Sciences, Computing Science, Mathematics and Engineering.

Computational Systems Biology is focused on the study of organisms from a holistic perspective. Computational design of biological systems is essential for allowing the construction of complex and large biological systems.

We provide a unique, multidisciplinary experience essential for understanding systems biology. The course draws together the highly-rated teaching and research expertise of our Schools of Computing Science, Mathematics and Statistics, Biology, and Cell and Molecular Biosciences. The course also has strong links with Newcastle's Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN).

Our course is designed for students from both biological and computational backgrounds. Prior experience with computers or computer programming is not required. Students with mathematical, engineering or other scientific backgrounds are also welcome to apply.

The course is part of a suite of related programmes that also include:
-Bioinformatics MSc
-Synthetic Biology MSc
-Computational Neuroscience and Neuroinformatics MSc

All four programmes share core modules, creating a tight-knit cohort. This encourages collaborations on projects undertaking interdisciplinary research.

Project work

Your five month research project gives you a real opportunity to develop your knowledge and skills in depth in Systems Biology. You have the opportunity to work closely with a leading research team in the School and there are opportunities to work on industry lead projects. You will have one-to-one supervision from an experienced member of the faculty, supported with supervision from associated senior researchers and industry partners as required.

The project can be carried out:
-With a research group at Newcastle University
-With an industrial sponsor
-With a research institute
-At your place of work

Placements

Students have a unique opportunity to complete a work placement with one of our industrial partners as part of their projects.

Previous students have found placements with organisations including:
-NHS Business Services Authority
-Waterstons
-Metropolitan Police
-Accenture
-IBM
-Network Rail
-Nissan
-GSK

Accreditation

We have a policy of seeking British Computer Society (BCS) accreditation for all of our degrees, so you can be assured that you will graduate with a degree that meets the standards set out by the IT industry. Studying a BCS-accredited degree provides the foundation for professional membership of the BCS on graduation and is the first step to becoming a chartered IT professional.

The School of Computing Science at Newcastle University is an accredited and a recognised Partner in the Network of Teaching Excellence in Computer Science.

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The Cognitive and Decision Sciences MSc at UCL studies the cognitive processes and representations underlying human thought, knowledge and decision-making. Read more
The Cognitive and Decision Sciences MSc at UCL studies the cognitive processes and representations underlying human thought, knowledge and decision-making. It integrates a wide range of disciplines and methodologies, with the core assumption that human cognition and choice are computational processes, implemented in neural hardware.

Degree information

Key topics include the nature of computational explanation; the general principles of cognition; the scope of rational choice explanation; probabilistic models of the mind; learning and memory; and applications to economics and business. The programme involves training in experimental design and methodology, building computational models and undertaking original research.

Students undertake modules to the value of 180 credits.

The programme consists of six core modules (90 credits), two optional modules (30 credits), and a research dissertation (60 credits).

Core modules
-Introduction to Cognitive Science
-Principles of Cognition
-Research Statistics
-Research Skills and Programming for Cognitive Science
-Judgement and Decision Making
-Knowledge, Learning and Inference

Optional modules
-Applied Decision-making
-Human Learning and Memory
-Cognitive Neuroscience
-Social Cognition: Research Methods
-The Brain in Action
-Neural Computation: Models of Brain Function
-Consumer Behaviour
-Understanding Individuals and Groups
-Social Neuroscience
-Social Cognition, Affect and Motivation
-Current Issues in Attitude Research
-Talent Management
-Business Psychology Seminars
-Interpretation of Forensic Evidence
-Consulting Psychology
-Designing and Analysing fMRI Experiments

Dissertation/report
All students undertake an independent research project which culminates in a dissertation of approximately 10,000–12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, seminars, class presentations, and practical, statistical, computational and experimental class work. Student performance is assessed through online tests, coursework, essays, practical experimental and computational mini-projects, and the dissertation.

Careers

Students have gone on to find employment in the following areas: research, teaching, lecturing, consultancy, finance, and marketing.

For more detailed careers information please visit the department website: http://www.ucl.ac.uk/pals/study/masters/TMSPSYSCDS01

Top career destinations for this degree:
-Managing Director, Temasek International Pte Ltd
-Consumer Behaviour Research Expert, TNS
-Insight Consultant, Kantar World Panel
-Assistant Policy Adviser, Cabinet Office Behavioural Insights Team
-Software Developer, Federal Home Loan Bank of New York

Employability
On completion of the programme, students will have acquired theoretical and empirical knowledge in cognition science and decision-making, and a broad range of practical research skills. They will have made original contributions to this field in their research projects, and will understand how to apply their knowledge to real-world decision problems. They will also have developed various analytical and logical reasoning skills which can be applied to many domains of research and non-academic work. They will, in addition, have an understanding of the philosophical issues underlying cognitive science and neuroscience.

Why study this degree at UCL?

The programme draws on an outstanding faculty, ranging across many disciplines, including internationally renowned researchers in psychology, computational modelling, neuroscience and economics.

London is one of the global hot-spots for research in cognition, decision-making, and neuroscience; and it is an intellectual hub, with a high density of research seminars and scientific meetings that attract leading international researchers.

London is also one of the world's foremost commercial and political centres, with consequent opportunities for high-level applied research; and it is a vibrant, culturally diverse and international city, with world-class music, theatre and galleries.

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This MRes is an innovative research-led programme which brings together expertise from across the Faculty of Brain Sciences and offers you the opportunity to work and train with leading researchers at one of the most highly regarded centres of excellence in brain science in the world. Read more
This MRes is an innovative research-led programme which brings together expertise from across the Faculty of Brain Sciences and offers you the opportunity to work and train with leading researchers at one of the most highly regarded centres of excellence in brain science in the world.

Degree information

Students will gain an understanding of the human brain and its disorders from the molecular to systems level that will reflect the interdisciplinary breadth of cutting-edge research in brain sciences conducted at UCL. Students will gain theoretical and practical knowledge of core personal and professional skills that underpin excellence in research.

Students undertake modules to the value of 180 credits.

The programme consists of three core modules (45 credits), one optional module (15 credits) and an extensive empirical research project (120 credits).

Core modules
-Research Methods I
-Research Methods II
-Contemporary Topics in Brain Sciences Research

Optional modules - students choose one of the following 15-credit optional modules:
-Cellular and Molecular Mechanisms of Disease
-Introduction to the Brain and Imaging the Brain
-Structure and Measurement of the Human Brain
-Introduction to Cognitive Science
-Principles of Cognition
-Molecular Pharmacology
-Developmental Neurobiology
-Receptors and Synaptic Signalling

Dissertation/research project
All students undertake an independent research project which culminates in a dissertation in the form of a journal article and an oral examination.

Teaching and learning
The programme is delivered through a combination of lectures, seminars, independent study, journal clubs, independent and collaborative problem-based tasks, practical demonstrations and classes, computational work, and a supervised empirical research project. Assessment is through online tasks, unseen written examinations, essays, oral presentations, research-based tasks and a primary research article.

Careers

This programme will prepare students for research careers in academia, industry or business, nationally or internationally. The first cohort of students on the Brain Sciences MRes will graduate after 2014, therefore no information on graduate destinations is currently available.

Employability
The programme provides a broad understanding of brain sciences. The aim is to give students the best chance of obtaining a place on a relevant PhD programme. In addition the programme includes taught elements that will enhance employability. Transferable skills include statistical training, communication skills, training in research ethics, research governance and in enterprise.

Why study this degree at UCL?

This comprehensive programme will provide core knowledge and skills, and ensure that prospective PhD candidates are thoroughly acquainted with the background as well as with the expanding scope of the field.

The unique curriculum will develop knowledge and insight into the broad and interdisciplinary scope of brain science through practical experience and exposure to contemporary topics in brain sciences research delivered through a series of innovative masterclasses led by internationally renowned researchers at UCL.

With an empirical research project encompassing two-thirds of the programme, quantitative and qualitative tools for research will be developed including core skills in the implementation, management and dissemination of research.

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This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

Degree information

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules
-Supervised Learning
-Statistical Modelling and Data Analysis
-Graphical Models or Probabilistic and Unsupervised Learning
Plus one of:
-Applied Bayesian Methods
-Statistical Design of Investigations
-Statistical Computing
-Statistical Inference

Optional modules - students select 60 credits from the following list:
-Advanced Topics in Machine Learning
-Affective Computing and Human-Robot Interaction
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Computational Modelling for Biomedical Imaging
-Information Retrieval and Data Mining
-Machine Vision
-Selected Topics in Statistics
-Optimisation
-Statistical Design of Investigations
-Statistical Inference
-Statistical Natural Language Programming
-Stochastic Methods in Finance
-Stochastic Methods in Finance 2
-Advanced Topics in Statistics
-Mathematical Programming and Research Methods
-Intelligent Systems in Business

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this degree:
-Statistical and Algorithm Analyst, Telemetry
-Decision Scientist, Everline
-Computer Vision Researcher, Slyce
-Data Scientist, YouGov
-Research Engineer, DeepMind

Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

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