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Masters Degrees (Mathematical Medicine)

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The MSc Mathematical Medicine and Biology will provide you with skills suitable for a research career in the exciting and growing field of mathematical medicine and biology. Read more

Overview

The MSc Mathematical Medicine and Biology will provide you with skills suitable for a research career in the exciting and growing field of mathematical medicine and biology.

You will take core modules in biology and the application of mathematics to medicine and biology. More advanced modules will introduce research topics in biomedical mathematics, including options in Computational Biology and Theoretical Neuroscience.

The taught training programme will be followed by a substantial individual project leading to a dissertation.

Throughout the course, the exceptional strength of the Centre for Mathematical Medicine and Biology will facilitate your hands-on experience of interdisciplinary biomedical research.

Some teaching activities will take place at the Sutton Bonington campus. The University provides a regular free hopper bus between University Park and Sutton Bonington.

Key facts:

- This course is informed by the work being carried out in the Centre for Mathematical Medicine and Biology.

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 50 full-time academic staff.

- In the latest independent Research Assessment Exercise, the school ranked eighth in the UK in terms of research power across the three subject areas within the School of Mathematical Sciences (pure mathematics, applied mathematics, statistics and operational research).

Module details

Biomolecular Data and Networks

Cell Structure and Function for Engineers

Computational and Systems Biology

Mathematical Medicine and Biology

Mathematical Medicine and Biology Dissertation

Practical Biomedical Modelling

Theoretical Neuroscience

Topics in Biomedical Mathematics

English language requirements for international students

IELTS: 6.0 (with no less than 5.5 in any element)

Further information



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The MPhil in History and Philosophy of Science and Medicine is a full-time 9-month course that provides students with the opportunity to carry out focused research under close supervision by senior members of the University. Read more
The MPhil in History and Philosophy of Science and Medicine is a full-time 9-month course that provides students with the opportunity to carry out focused research under close supervision by senior members of the University. Students will acquire or develop skills and expertise relevant to their research interests, as well as a critical and well informed understanding of the roles of the sciences in society. Those intending to go on to doctoral work will learn the research skills needed to help them prepare a well planned and focused PhD proposal. During the course students gain experience of presenting their own work and discussing the issues that arise from it with an audience of their peers and senior members of the Department; they will attend lectures, supervisions and research seminars in a range of technical and specialist subjects central to research in the different areas of History and Philosophy of Science and Medicine.

The educational aims of the programme are:

- to give students with relevant training at first-degree level the opportunity to carry out focussed research in History, Philosophy of Science and Medicine under close supervision;
- to give students the opportunity to acquire or develop skills and expertise relevant to their research interests;
- to enable students to acquire a critical and well informed understanding of the roles of the sciences in society; and
- to help students intending to go on to doctoral work to acquire the requisite research skills and to prepare a well planned and focussed PhD proposal.

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

Course detail

The MPhil course is taught by supervisions and seminars and assessed by three research essays and a dissertation.

The topics of the essays and dissertation should each fall within the following specified subject areas:

1. General philosophy of science
2. History of ancient and medieval science, technology and medicine
3. History of early modern science, technology and medicine
4. History of modern science, technology and medicine
5. History, philosophy and sociology of the life sciences
6. History, philosophy and sociology of the physical and mathematical sciences
7. History, philosophy and sociology of the social and psychological sciences
8. History, philosophy and sociology of medicine
9. Ethics and politics of science
10. History and methodology of history, philosophy and sociology of science, technology and medicine

Format

The MPhil seminars are the core teaching resource for this course. In the first part of year these seminars are led by different senior members of the Department and focus on selected readings. During the rest of the year the seminars provide opportunities for MPhil students to present their own work.

Students are encouraged to attend the lectures, research seminars, workshops and reading groups that make the Department a hive of intellectual activity. The Department also offers graduate training workshops, which focus on key research, presentation, publication and employment skills.

The MPhil programme is administered by the MPhil Manager, who meets all new MPhil students as a group in early October, then sees each of the students individually to discuss their proposed essay and dissertation topics. The Manager is responsible for finding appropriate supervisors for each of these topics; the supervisors are then responsible for helping the student do the research and writing needed for the essays and the dissertation. Students will see each of their supervisors frequently; the MPhil Manager sees each student at regular intervals during the year to discuss progress and offer help and advice.

Supervisions are designed to provide students with the opportunity to set their own agenda for their studies. The supervisor's job is to support the student's research, not to grade their work – supervisors are formally excluded from the examination process.

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

Learning Outcomes

By the end of the course, students will have:

- Knowledge and Understanding -

- developed a deeper knowledge of their chosen areas of History, Philosophy of Science and Medicine and of the critical debates within them;
- acquired a conceptual understanding that enables the evaluation of current research and methodologies;
- formed a critical view of the roles of the sciences in society.

- Skills and other attributes -

By the end of the course students should have:

- acquired or consolidated historiographic, linguistic, technical and ancillary skills appropriate for research in their chosen area;
- demonstrated independent judgement, based on their own research;
- presented their own ideas in a public forum and learned to contribute constructively within an international environment.

Assessment

- A dissertation of up to 15,000 words. Examiners may request an oral examination but this is not normally required.
- Three essays, each of up to 5,000 words.

Students receive independent reports from two examiners on each of their three essays and the dissertation.

Continuing

The usual preconditions for continuing to the PhD are an overall first class mark in the MPhil, a satisfactory performance in an interview and agreement of the PhD proposal with a potential supervisor.

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

Funding Opportunities

- Rausing Studentships
- Raymond and Edith Williamson Studentships
- Lipton Studentships
- Wellcome Master's Awards

Please see the Department's graduate funding page for more information: http://www.hps.cam.ac.uk/studying/graduate/funding.html

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

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Oxford’s new MSc in Nanotechnology for Medicine and Health Care builds on the world-leading research in nanomedicine at the University’s Institute of Biomedical Engineering and other departments in the Mathematical, Physical and Life Sciences (MPLS) Division and Medical Sciences Division. Read more
Oxford’s new MSc in Nanotechnology for Medicine and Health Care builds on the world-leading research in nanomedicine at the University’s Institute of Biomedical Engineering and other departments in the Mathematical, Physical and Life Sciences (MPLS) Division and Medical Sciences Division. This advanced modular course is delivered by leading scientists and experts in this rapidly developing field and has been specifically designed for those who would value a part-time modular learning structure, for example those in full-time employment, both in the UK and overseas.

The MSc is designed to be completed part-time, normally over a two- to three-year period, and so provides a path to career development that is flexible and recognised within academia and industry. The programme comprises three online modules exploring the fundamentals of science and materials characterisation at the nanoscale, three intensive five-day face-to-face modules describing the clinical and commercial application of such science, and a piece of original lab-based research leading to the submission of a dissertation. This modular structure provides an adaptable approach to learning, and each module may also be taken as an individual short course.

There are opportunities to access and learn about cutting-edge research and current practice in a wide range of nanotechnology and healthcare topics from experts with experience of taking nanotechnologies from basic concept through clinical validation to market realisation. The tutor-led approach lends cohesion to the modular experience which is tailored for busy people in full-time employment who wish to minimise time away from the workplace to study.

Visit the website https://www.conted.ox.ac.uk/about/msc-in-nanotechnology-for-medicine-and-health-care

The first deadline for applications is Friday 20 January 2017

If your application is completed by this January deadline and you fulfil the eligibility criteria, you will be automatically considered for a graduate scholarship. For full details please see: http://www.ox.ac.uk/admissions/graduate/fees-and-funding/graduate-scholarships.

Description

Nanotechnology is the production and application of devices and systems at the nanometre scale, which is of the order of one billionth of a metre. Developments in this area of technology are now coming to fruition, and increasingly impacting on our daily lives. In particular, nanotechnology is becoming a crucial driving force behind innovation in medicine and healthcare, with a range of advances including nanoscale therapeutics, biosensors, implantable devices and imaging systems. However, the pace with which this revolution is occurring has left even some of its leading practitioners lacking in aspects of the key fundamental knowledge or the information required to navigate the regulatory and clinical pathway to achieve market realisation.

The University of Oxford's MSc in Nanotechnology for Medicine and Health Care offers a detailed and cutting-edge education in this subject and builds on the successful Postgraduate Certificate in Nanotechnology, which was launched in 2006. The course is taken part-time as a mixture of online and face-to-face modules, meaning it can fit around the demands of those working full-time and can be studied by international students without the requirement to relocate. The course uses a blend of individual study of learning materials, together with group work during live online tutorials, conventional lectures and discussions and also requires the student to submit a dissertation reporting an original piece of nanomedicine-based research. The group sessions with tutors are particularly valuable because they offer highly focused learning and assessment opportunities.

Programme details

The MSc in Nanotechnology for Medicine and Health Care is a part-time course consisting of six modules and a research project and associated dissertation. The programme is normally completed in two to three years. Students are full members of the University of Oxford and are matriculated as members of an Oxford college.

The modules in this programme can also be taken as individual short courses. It is possible to transfer credit from up to three previously completed modules into the MSc programme, if the time elapsed between commencement of the accredited module(s) and registration on the MSc is not more than two years.

The course comprises:

- three online modules giving a thorough introduction to the fundamental science of nanotechnology and the behaviour and characterisation of nanoscale materials;

- three five-day modules taught face-to-face in Oxford explaining the scientific, regulatory, clinical and commercial aspects of the application of nanotechnology to medicine and healthcare

- an original research project of approximately 18 weeks to be written up as a dissertation

The course has a dedicated Course Director, Associate Director and administration team accustomed to supporting students undertaking distance learning and face-to-face courses. Students have access to staff at the University of Oxford’s Begbroke Science Park and Institute of Biomedical Engineering, particularly the Course Director, Professor Robert Carlisle and the Associate Course Director, Dr Christiane Norenberg.

Throughout the course, students can use the University of Oxford’s excellent electronic library resources to enable them to complete the assignment tasks.

Programme modules:

- Module 1: The Wider Context of Nanotechnology (online)
- Module 2: The Fundamental Science of Nanotechnology (online)
- Module 3: Fundamental Characterisation for Nanotechnology (online with two-day component in Oxford)
- Module 4: Introduction to Bionanotechnology (in Oxford)
- Module 5: Nanomedicine – Science and Applications (in Oxford)
- Module 6: Clinical Translation and Commercialisation of Nanomedicine (in Oxford)

To complete the MSc, students need to attend the six modules and complete the assessed written assignments for each module, and complete a research project with dissertation on a topic chosen in consultation with a supervisor and the Course Director.

Who is it for?

This is a part-time, modular course leading to a postgraduate qualification at the University of Oxford. The course is designed for students wishing to study part-time. It will appeal to those working in the commercial, research or healthcare sectors who use or develop nanotechnology in their work. Applications are welcome from biomedical engineers, materials scientists, biotech-entrepreneurs, medical practitioners, chemists, pharmacists, electrical engineers, project managers in related industries, patent agents, legislators, as well as those involved in commercial or academic research in this area of science.

Find out how to apply here - http://www.ox.ac.uk/admissions/graduate/applying-to-oxford

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Climate change is recognised as having potentially huge impacts on the environment and on human society. Read more
Climate change is recognised as having potentially huge impacts on the environment and on human society. This programme aims to provide an understanding of climate change causes, impacts, mitigation and adaptation measures from a life science perspective in conjunction with developing a wide variety of mathematical modelling skills that can be used to investigate the impacts of climate change.

The programme closely follows the structure of our Applied Mathematical Sciences MSc. Two of the mandatory courses will specifically focus on understanding the issues related to climate change and are taught by the School of Life Sciences.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Mathematical Ecology;
Climate Change: Causes and Impacts;
Climate Change: Mitigation and Adaptation Measures;
Dynamical Systems (recommended);
Stochastic Simulation (recommended)

Optional Courses

Optimization;
Mathematical Biology and Medicine;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Applied Linear Algebra;
Partial Differential Equations;
Numerical Analysis;
Geometry;
Bayesian Inference.

Typical project subjects

Population Cycles of Forest Insects;
Climate Change Impact;
The replacement of Red Squirrels by Grey Squirrels in the UK;
Vegetation Patterns in Semi-arid Environments;
Daisyworld: A Simple Land Surface Climate Model.

The final part of the MSc is an extended project in mathematical modelling the impacts of climate change on environmental systems, giving the opportunity to investigate a topic in some depth guided by leading research academics.

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Applied Mathematical Sciences offers a clear and relevant gateway into a successful career in business, education or scientific research. Read more
Applied Mathematical Sciences offers a clear and relevant gateway into a successful career in business, education or scientific research. The programme arms students with the essential knowledge required by all professional mathematicians working across many disciplines. You will learn to communicate their ideas effectively to peers and others, as well as the importance of research, planning and self-motivation.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

:

Modelling and Tools;
Optimization;
Dynamical Systems;
Applied Mathematics (recommended);
Applied Linear Algebra (recommended).

Optional Courses

:

Mathematical Ecology;
Functional Analysis;
Numerical Analysis of ODEs;
Pure Mathematics;
Statistical Methods;
Stochastic Simulation;
Software Engineering Foundations;
Mathematical Biology and Medicine;
Partial Differential Equations;
Numerical Analysis;
Geometry.

Typical project subjects

:

Pattern Formation of Whole Ecosystems;
Climate Change Impact;
Modelling Invasive Tumour Growth;
Simulation of Granular Flow and Growing Sandpiles;
Finite Element Discretisation of ODEs and PDEs;
Domain Decomposition;
Mathematical Modelling of Crime;
The Geometry of Point Particles;
Can we Trust Eigenvalues on a Computer?

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An MSc is generally accepted as being highly desirable for starting and developing a career in Medical Statistics. The MSc in Statistics with Applications in Medicine is also an excellent preparation for embarking on a PhD project in Statistics or Medical Statistics. Read more

An MSc is generally accepted as being highly desirable for starting and developing a career in Medical Statistics. The MSc in Statistics with Applications in Medicine is also an excellent preparation for embarking on a PhD project in Statistics or Medical Statistics.

The MSc in Statistics with Applications in Medicine, taught by one of the largest and strongest Statistics groups in the UK, will provide you with a sound Masters-level training in Statistical methodology, with an emphasis on practical problems arising in the context of collecting and analysing Medical data. Several modules are delivered by Medical Statisticians, who can provide data and case studies from their own day to day work at Southampton General Hospital and the Medical Research Council Lifecourse Epidemiology Unit.

While studying for your degree, you will develop key transferrable skills, such as written and oral communication, the use of and some programming in Statistical software, time management, and basic research skills. 

Programme objectives are:

  • to give you knowledge of statistical theory and methods at an advanced level
  • to train you for a career as a statistician, particularly in areas related to medicine
  • to give you experience of applications of statistical methods
  • to enable you to develop oral and written communication skills

Careers:

Past graduates have joined major pharmaceutical companies, research teams at the Medical Research Council, university-based medical research units, contract research organisations, government, the financial sector, or have continued with further study to become successful PhD students.

Introducing your course

The ever-increasing amount and range of patient data presents the pharmaceutical industry and medical research institutions with significant challenges and great opportunities. Medical Statisticians design and analyse clinical trials for new treatments; they help to identify the genes responsible for disease and they developing methodology to enable advances in personalised medicine. 

Their work underpins scientific breakthroughs that will be life-saving for many. In the MSc in Statistics with Applications in Medicine you will examine new developments in challenging medical data problems through the study of clinical trials, statistical genetics and epidemiological methods.

Overview

The full-time MSc is completed over a 12-month period. There are two semesters of taught material, which account for 60 ECTS credits, followed by the MSc project in summer, which accounts for 30 ECTS credits.

The programme structure allows you to select options ranging from the more theoretical aspects of Statistics, including a module on research topics, to those which cover material focussed on practical applications of Statistics in a clinical setting. This is complemented by modules on research skills, a Medical Statistics seminar series providing insight into the role of Medical Statisticians in various different careers (which also gives opportunities for networking with the speakers), and several presentations on transferrable skills by the University Careers and Employability Service Team.

View the programme specification document for this course



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he contribution of mathematical and computational modelling to the understanding of biological systems has rapidly grown in recent years. Read more
he contribution of mathematical and computational modelling to the understanding of biological systems has rapidly grown in recent years. This discipline encompasses a wide range of life science areas, including ecology (e.g. population dynamics), epidemiology (e.g. spread of diseases), medicine (e.g. modelling cancer growth and treatment) and developmental biology.

This programme aims to equip students with the necessary technical skills to develop, analyse and interpret models applied to biological systems. Course work is supported by an extended and supervised project in life science modelling.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Mathematical Ecology;
Dynamical Systems;
Mathematical Biology and Medicine.

Optional Courses

Optimization;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Stochastic Simulation;
Partial Differential Equations;
Numerical Analysis;
Geometry;
Climate Change: Causes and Impacts;
Biologically Inspired Computation;
Climate Change: Mitigation and Adaptation Measures.

Typical project subjects

Population Cycles of Forest Insects;
Modelling Invasive Tumour Growth;
The replacement of Red Squirrels by Grey Squirrels in the UK;
Wiring of Nervous System;
Vegetation Patterning in Semi-arid Environments;
Daisyworld: A Simple Land Surface Climate Model.

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About the programme. In addition to being a science in its own right, mathematics plays a fundamental role in the quantitative areas of practically all other academic disciplines, particularly in the natural sciences, engineering, business administration, economics, medicine and psychology. Read more

About the programme

In addition to being a science in its own right, mathematics plays a fundamental role in the quantitative areas of practically all other academic disciplines, particularly in the natural sciences, engineering, business administration, economics, medicine and psychology. Mathematical results permeate nearly all facets of life and are a necessary prerequisite for the vast majority of modern technologies – and as our IT systems become increasingly powerful, we are able to mathematically handle enormous amounts of data and solve ever more complex problems.

Special emphasis is placed on developing students' ability to formalise given problems in a way that facilitates algorithmic processing as well as enabling them to choose or develop, and subsequently apply, suitable algorithms to solve problems in an appropriate manner. The degree programme is theoretical in its orientation, with strongly application-oriented components. Studying this programme, you can gain advanced knowledge in the mathematical areas of Cryptography, Computer Algebra, Algorithmic Algebra and Geometry, Image and Signals Processing, Statistics and Stochastic Simulation, Dynamical Systems and Control Theory as well as expert knowledge in Computer Science fields such as Data Management, Machine Learning and Data Mining.

Furthermore, you will have the chance to learn how to apply your knowledge to tackle problems in areas as diverse as Marketing, Predictive Analytics, Computational Finance, Digital Humanities, IT Security and Robotics.

Programme syllabus

The core modules consist of two mathematics seminars and the presentation of your master's thesis.The compulsory elective modules are divided into eight module groups:

1)   Algebra, Geometry and Cryptography

This module group imparts advanced results in the areas of algebra and geometry, which constitute the fundament for algorithmic calculations, particularly in cryptography but also in many other mathematical areas.

2)   Mathematical Logic and Discrete Mathematics

The theoretical possibilities and limitations of algorithm-based solutions are treated in this module group.

3)   Analysis, Numerics and Approximation Theory

Methods from the fields of mathematical analysis, applied harmonic analysis and approximation theory for modelling and approximating continuous and discrete data and systems as well as efficient numerical implementation and evaluation of these methods are the scope of this module group.

4) Dynamical Systems and Optimisation

Dynamical systems theory deals with the description of change over time. This module group is concerned with methods used for the modelling, analysis, optimisation and design of dynamical systems, as well as the numerical implementation of such techniques.

5) Stochastics, Statistics

This module group deals with methods for modelling and analysing complex random phenomena as well as the construction, analysis and optimisation of stochastic algorithms and techniques used in statistical data analysis.

6) Data Analysis and Data Management and Programming

This module group examines the core methods used in computer science for the analysis of data of heterogeneous modalities (e.g. multimedia data, social networks and sensor data) and for the realisation of data analysis systems.

7) Applications

In this module group, you will practise applying the mathematical methods learned in module groups 1 to 6 to real-world applications such as Marketing, Predictive Analytics and Computational Finance.

8) Key Competencies and Language Training

In this module group, you will choose seminars that develop your non-subject-specific skills, such as public speaking and academic writing and other soft skills; you may also undertake internships. This serves to complement your technical expertise gained during your degree studies and helps to prepare you for your professional life after university.



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Goal of the pro­gramme. Life Sciences.  is one of the strategic research fields at the University of Helsinki. The multidisciplinary Master’s Programme in Life Science Informatics (LSI) integrates research excellence and research infrastructures in the Helsinki Institute of Life Sciences (. Read more

Goal of the pro­gramme

Life Sciences is one of the strategic research fields at the University of Helsinki. The multidisciplinary Master’s Programme in Life Science Informatics (LSI) integrates research excellence and research infrastructures in the Helsinki Institute of Life Sciences (HiLIFE).

The Master's Programme is offered by the Faculty of Science. Teaching is offered in co-operation with the Faculty of Medicine and the Faculty of Biological and Environmental Sciences. As a student, you will gain access to active research communities on three campuses: Kumpula, Viikki, and Meilahti. The unique combination of study opportunities tailored from the offering of the three campuses provides an attractive educational profile. The LSI programme is designed for students with a background in mathematics, computer science and statistics, as well as for students with these disciplines as a minor in their bachelor’s degree, with their major being, for example, ecology, evolutionary biology or genetics. As a graduate of the LSI programme you will:

  • Have first class knowledge and capabilities for a career in life science research and in expert duties in the public and private sectors
  • Competence to work as a member of a group of experts
  • Have understanding of the regulatory and ethical aspects of scientific research
  • Have excellent communication and interpersonal skills for employment in an international and interdisciplinary professional setting
  • Understand the general principles of mathematical modelling, computational, probabilistic and statistical analysis of biological data, and be an expert in one specific specialisation area of the LSI programme
  • Understand the logical reasoning behind experimental sciences and be able to critically assess research-based information
  • Have mastered scientific research, making systematic use of investigation or experimentation to discover new knowledge
  • Have the ability to report results in a clear and understandable manner for different target groups
  • Have good opportunities to continue your studies for a doctoral degree

Further information about the studies on the Master's programme website.

Pro­gramme con­tents

The Life Science Informatics Master’s Programme has six specialisation areas, each anchored in its own research group or groups.

Algorithmic bioinformatics with the Genome-scale algorithmicsCombinatorial Pattern Matching, and Practical Algorithms and Data Structures on Strings research groups. This specialisation area educates you to be an algorithm expert who can turn biological questions into appropriate challenges for computational data analysis. In addition to the tailored algorithm studies for analysing molecular biology measurement data, the curriculum includes general algorithm and machine learning studies offered by the Master's Programmes in Computer Science and Data Science.

Applied bioinformaticsjointly with The Institute of Biotechnology and genetics.Bioinformatics has become an integral part of biological research, where innovative computational approaches are often required to achieve high-impact findings in an increasingly data-dense environment. Studies in applied bioinformatics prepare you for a post as a bioinformatics expert in a genomics research lab, working with processing, analysing and interpreting Next-Generation Sequencing (NGS) data, and working with integrated analysis of genomic and other biological data, and population genetics.

Biomathematics with the Biomathematics research group, focusing on mathematical modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of topics ranging from problems at the molecular level to the structure of populations. To tackle these problems, the research group uses a variety of modelling approaches, most importantly ordinary and partial differential equations, integral equations and stochastic processes. A successful analysis of the models requires the study of pure research in, for instance, the theory of infinite dimensional dynamical systems; such research is also carried out by the group. 

Biostatistics and bioinformatics is offered jointly by the statistics curriculum, the Master´s Programme in Mathematics and Statistics and the research groups Statistical and Translational GeneticsComputational Genomics and Computational Systems Medicine in FIMM. Topics and themes include statistical, especially Bayesian methodologies for the life sciences, with research focusing on modelling and analysis of biological phenomena and processes. The research covers a wide spectrum of collaborative topics in various biomedical disciplines. In particular, research and teaching address questions of population genetics, phylogenetic inference, genome-wide association studies and epidemiology of complex diseases.  

Eco-evolutionary Informatics with ecology and evolutionary biology, in which several researchers and teachers have a background in mathematics, statistics and computer science. Ecology studies the distribution and abundance of species, and their interactions with other species and the environment. Evolutionary biology studies processes supporting biodiversity on different levels from genes to populations and ecosystems. These sciences have a key role in responding to global environmental challenges. Mathematical and statistical modelling, computer science and bioinformatics have an important role in research and teaching.

Systems biology and medicine with the Genome-scale Biology Research Program in BiomedicumThe focus is to understand and find effective means to overcome drug resistance in cancers. The approach is to use systems biology, i.e., integration of large and complex molecular and clinical data (big data) from cancer patients with computational methods and wet lab experiments, to identify efficient patient-specific therapeutic targets. Particular interest is focused on developing and applying machine learning based methods that enable integration of various types of molecular data (DNA, RNA, proteomics, etc.) to clinical information.



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Accurate and efficient scientific computations lie at the heart of most cross-discipline collaborations. It is key that such computations are performed in a stable, efficient manner and that the numerics converge to the true solutions, dynamics of the physics, chemistry or biology in the problem. Read more
Accurate and efficient scientific computations lie at the heart of most cross-discipline collaborations. It is key that such computations are performed in a stable, efficient manner and that the numerics converge to the true solutions, dynamics of the physics, chemistry or biology in the problem.

The programme closely follows the structure of our Applied Mathematical Sciences MSc and will equip you with the skill to perform efficient accurate computer simulations in a wide variety of applied mathematics, physics, chemical and industrial problems.

Students will take a total of 8 courses, 4 in each of the 1st and 2nd Semesters followed by a 3-month Project in the summer. A typical distribution for this programme is as follows:

Core courses

Modelling and Tools;
Stochastic Simulation;
Applied Linear Algebra;
Numerical Analysis;

Optional Courses

Dynamical Systems;
Optimization;
Partial Differential Equations;
Numerical Analysis of ODEs;
Applied Mathematics;
Statistical Methods;
Functional Analysis;
Software Engineering Foundations;
Mathematical Biology and Medicine;
Biologically Inspired Computation;
Advanced Software Engineering;
Geometry;
Bayesian Inference;

Typical project subjects

Simulation of Granular Flow and Growing Sandpiles;
Finite Element Discretisation of ODEs and PDEs;
Domain Decomposition;
Computational Spectral Theory;
Mathematical Modelling of Crime;
Mathematical Modelling of Micro-electron Mechanical Systems.
Can we Trust Eigenvalues on a Computer?

The final part of the MSc is an extended project in computational mathematics, giving the opportunity to investigate a topic in some depth guided by leading research academics from our 5-rated mathematics and statistics groups.

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The Modelling Biological Complexity MRes is designed for students who wish to develop the skills to apply mathematical, computational and physical science techniques to real biological problems. Read more

The Modelling Biological Complexity MRes is designed for students who wish to develop the skills to apply mathematical, computational and physical science techniques to real biological problems. The programme provides a broad overview of the cutting edge research at the interface of the life, mathematical and physical sciences.

About this degree

Foundation courses use innovative teaching methods for interdisciplinary research to provide essential background knowledge in mathematical, computational and physical techniques and a broad introduction to core biological concepts and systems. A range of interdisciplinary research-driven projects follow in which students gain experience of different research techniques and a range of areas of biological interest.

Students undertake modules to the value of 180 credits.

The programme consists of four compulsory modules: foundation courses module, transferable skills module (20%), three mini projects (40%) and a research (summer) project (40%).

Core modules

  • Modelling Biological Complexity: Foundation Course (non credit bearing)
  • Transferable and Generic Skills
  • Mini projects
  • Research (summer) Project

There are no optional modules for this programme.

Dissertation/report

All students undertake an independent research (summer) project, which culminates in a dissertation of up to 15,000 words, a short presentation and an oral examination.

Teaching and learning

The programme is delivered through a combination of lectures, laboratory work, case presentations, seminars, tutorials and project work. Student performance is assessed by essays, mini projects, oral and poster presentations, a computer programming and biological database task, web development, the research project and an end-of-year viva.

Further information on modules and degree structure is available on the department website: Modelling Biological Complexity MRes

Careers

After passing the MRes, students may have the opportunity to progress onto a PhD at UCL.

Employability

CoMPLEX has built upon relationships with partners within academia and industry, to develop our existing CoMPLEX programme. so that it continues to be designed specifically to provide training that meets market needs. Graduates have excellent publication outputs, this, together with CoMPLEX's international reputation means that graduates are and will continue to be recognised when entering the job market. 70% of recent graduates have taken up positions in research centres in the UK and abroad. As small number have pursued careers in science policy analysis, cyber security, science teaching, statistical and mathematical consultancy, technology consultancy, or in management and the financial sector.

Why study this degree at UCL?

CoMPLEX is UCL's centre for interdisciplinary research in the life sciences. It brings together life and medical scientists with computer scientists, mathematicians, physicists and engineers to tackle the challenges arising from complexity in biology and medicine.

CoMPLEX collaborates with 250+ supervisors from 40 UCL Departments and maintains strong links with leading UK/International research institutions, charities and industrial partners e.g. AstraZeneca, British Heart Foundation, CRUK, Francis Crick Institute, GlaxoSmithKline, Microsoft Research and Renishaw. As a result CoMPLEX students have a vast range of projects to choose from and the opportunity to network with a plethora of scientific partners.

Peer-to-peer learning is a crucial part of the training, and students will take part in cohort activities, such as, mentoring events, a seminar series, outreach groups and an annual retreat.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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Mathematics is a core scientific subject and an essential basis for a range of other sciences. Read more
Mathematics is a core scientific subject and an essential basis for a range of other sciences. This programme brings together the latest developments in a range of mathematical disciplines to provide you with a thorough grounding in the subject, together with a substantial project that can be used to develop a specialisation.

Internationally leading research supports this programme, with particular research strengths including magnetic fields, interface of algebraic number theory and abstract algebra, climate system dynamics and display-structure on crystalline cohomology.
The programme prepares you for a career in numerous industries or for progression to a PhD for those interested in pursuing a research pathway.

Programme structure

The programme comprises three compulsory taught modules and 90 credits of option modules. The taught component of the programme is completed in June with the project extending over the summer period for submission in September.

Compulsory Modules

The compulsory modules can include; Research in Mathematical Sciences; Advanced Mathematics Project and Analysis and Computation for Finance

Optional Modules

Some examples of the optional modules are as follows;
Logic and Philosophy of Mathematics; Methods for Stochastics and Finance; Mathematical Theory of Option Pricing; Dynamical Systems and Chaos; Fluid Dynamics of Atmospheres and Oceans; Modelling the Weather and Climate; The Climate System; Algebraic Number Theory; Algebraic Curves; Waves, Instabilities and Turbulence; Magnetic Fields and Fluid Flows; Statistical Modelling in Space and Time and Mathematical Modelling in Biology and Medicine.

The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.

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Infectious diseases remain a major contributor to the global burden of disease, with HIV, malaria, measles, diarrhoeal disease and respiratory infections responsible for over 50% of premature deaths worldwide. Read more
Infectious diseases remain a major contributor to the global burden of disease, with HIV, malaria, measles, diarrhoeal disease and respiratory infections responsible for over 50% of premature deaths worldwide. However the availability of resources for interventions is limited in comparison with the scale of the challenges faced. Over the last decade there has been increasing recognition of the value of epidemiological analysis and mathematical modelling in aiding the design and interpretation of clinical trials from a population perspective and, downstream, to guide implementation, monitoring and evaluation of intervention effectiveness. The Epidemiology, Evolution and Control of Infectious Diseases (EECID) stream provides a research-based training in infectious disease epidemiology, mathematical modelling and statistics, genetics and evolution, and computational methods. The focus of the course is inter-disciplinary, with a strong applied public health element.

Based in the Department of Infectious Disease Epidemiology in the Faculty of Medicine, the stream provides an opportunity to learn, in a supportive and stimulating environment, from leaders in the field who are actively engaged in research and advise leading public health professionals, policy-makers, governments, international organisations and pharmaceutical companies, both nationally and internationally, on a range of diseases include pandemic influenza, HIV, TB, malaria, polio and neglected tropical diseases (NTDs).

This stream is linked to the Wellcome Trust 4-year PhD programme in the Epidemiology, Evolution and Control of Infectious Diseases which includes up to 5 funded studentships each year. Up to 3 further 1+3 MRC studentships are also available each year.

The emphasis of the course will be to provide a thorough training in epidemiology, mathematical modelling and statistics, and genetics and evolution, as applied to infectious diseases. This research-orientated training will incorporate taught material, practical sessions in statistical software (R) and C programming as well as wider generic training in the research and communication skills needed to interact with public health agencies. Through the two research-based projects students will be exposed to the latest developments in the field and will gain first-hand experience in applying the methods they are taught to questions of public-health relevance.

Individuals who complete the course will have developed the ability to:

-Describe the biology, epidemiology and control of major global infectious diseases
-Interpret and present epidemiological data
-Undertake statistical analysis of infectious disease data including applying modern methods for statistical inference
-Develop and apply mathematical models to understand infectious disease dynamics, evolution and control
-Analyse genetic data using modern techniques and interpret their relevance to infectious disease epidemiology
-Critically evaluate research papers and reports
-Write and defend research reports and publications
-Communicate effectively through writing, oral presentations and IT to facilitate further study or employment in epidemiology and public health
-Exercise a range of transferable skills

This will be achieved through a course of lectures, seminars, tutorials and technical workshops. Please note that Postgraduate Diplomas and Certificates for part-completion are not available for this course.

The stream will be based in the Department of Infectious Disease Epidemiology on the St Mary’s campus of Imperial College London.

Each student chooses two projects over the course of the year from the wide range available. Students are guided in this choice by the stream organiser and their personal tutor and are advised to take contrasting projects to ensure a balanced training.

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The College of Liberal Arts and Sciences is a thriving center of intellectual excellence that encompasses 14 academic departments and 80 degree programs. Read more
The College of Liberal Arts and Sciences is a thriving center of intellectual excellence that encompasses 14 academic departments and 80 degree programs. Its more than 2,500 students are engaged in a wide variety of challenging courses and hands-on learning experiences that extend across all areas of the humanities and sciences – from the great philosophers and classic literature to the world economy and environmental sustainability.

At the core of each department are faculty members who have garnered national acclaim for their best-selling books, ground-breaking research and creative endeavors. Together, students and their professors explore globally significant subjects and work towards the goal of improving every aspect of the way in which human beings live. To learn more about a specific area of study, click on the left-hand navigation bar for a full listing of academic departments.

The department

The Department of Mathematics provides numerous undergraduate and graduate level courses that will enable you to master the mathematical methods and sophisticated reasoning and problem-solving skills essential to a wide variety of fields. In addition, the department offers a program to become an actuary.

The bachelor’s and master’s degree programs are designed to provide flexibility while emphasizing mathematical reasoning and problem solving, preparing the student for graduate school or a career in mathematics in secondary school teaching, business, industry, government or academia. In addition, a degree in mathematics is regarded as excellent preparation for entrance to professional schools of law, medicine or business.

M.S. in Mathematics for Secondary School Teachers

A high school teacher with an advanced knowledge of mathematics can make a profound impact on his or her students. A sophisticated understanding of mathematical concepts and problem-solving strategies can help bring high school-level math vividly to life for the adolescent student.

The M.S. in Mathematics for Secondary School Teachers is designed for people who are currently working as teachers or those who plan to enter the teaching field. This program makes the mathematics teacher more versatile and valuable to his or her school district.
The 37-credit Master of Science program consists of 25 credits of required courses covering such subjects as set theory, mathematical logic and information, abstract algebra, Euclidean geometry, history of mathematics and the foundations and applications of analysis. It also offers the opportunity to satisfy individual interests by requiring 12 credits of electives. As a capstone project, students either write a thesis or prepare a lecture on mathematics suitable for high school students.

Note: This program does not lead to New York State teaching certification

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Our MSc Model-based Drug Development course provides the knowledge and skills for making evidence-based decisions at various stages of drug development. Read more

Our MSc Model-based Drug Development course provides the knowledge and skills for making evidence-based decisions at various stages of drug development.

It covers the scientific and regulatory aspects of evaluating a drug, with emphasis on the use of modelling and simulation methods. You will learn why these methods are so highly valued by industry and regulatory authorities as effective, cost-saving, decision-making tools. Learning is reinforced via hands-on application of the skills to real data.

The course has been developed with an emphasis on mechanistic approaches to assessing and predicting pharmacokinetics and pharmacodynamics (PKPD), such as physiologically-based pharmacokinetics (PBPK) .

As this comes under the general umbrella of systems biology, you will be able to apply your knowledge of modelling and simulation in various areas of research within the pharmaceutical industry.

Full-time students benefit from immersion in the varied biomedical research environment at The University of Manchester, including interaction with research staff at the renowned Centre for Applied Pharmacokinetic Research .

Alternatively, part-time students already working in the pharmaceutical industry can take advantage of the flexible, distance learning mode of the course, which allows you to fit study around other commitments.

Aims

The aim of the course is to provide specialist knowledge and skills that are highly relevant for a career linked to drug development and pharmaceutical industry.

It is designed for science, engineering or mathematics graduates who want to acquire:

  • awareness of the commercial and regulatory factors in drug development;
  • understanding of the physiological, chemical, and mathematical foundations used to define the safe and effective use of potential medicines;
  • training in the use of mathematical modelling and simulation methods to guide drug development.

The course aims to:

  • provide background information on the theory and methods for quantitative assessment of drug absorption, distribution and elimination;
  • provide an understanding of the role of pharmacometrics in the process of drug development;
  • provide background information on in vitro assays used to characterise ADME properties of new drug entities;
  • indicate the mathematical framework that is capable of integrating in vitro information with knowledge of the human body to predict pharmacokinetics;
  • provide familiarity and experience of using different software platforms related to pharmacometric data analysis including R, Phoenix, NONMEM, MATLAB, Simcyp, WinBUGS and MONOLIX;
  • equip you to reflect upon influential research publications in the field, to critically assess recent published literature in a specific area;
  • provide awareness of the elements of a convincing research proposal based on modelling and simulation;
  • provide the opportunity to undertake a project and carry out original research.

Special features

Distance learning option

Our distance learning option is ideal for scientists linked to the pharmaceutical industry who wish to expand their expertise while working in the industry.

Full-time mode

The full-time mode allows suitably trained mathematics, science or engineering graduates to focus on obtaining the advanced skills needed for a career in this area. We utilise a blended learning approach in which online learning content is supported by regular face-to-face contact with tutors.

Hands-on learning

Your learning will be reinforced over the duration of the course via hands-on application of your skills to real data.

Additional course information

The course focuses on the following topics.

  • Pharmacokinetics: addressing how a drug dose is administered to the body and the fate of drug molecules that enter the body.
  • Pharmacodynamics: addressing the chemical and physiological response of the body to drug.
  • Pharmacometrics: the science that quantifies drug, disease and trial information to aid efficient drug development and/or regulatory decisions (definition used by the US FDA).
  • Systems pharmacology: analysis of interactions between drug and a biological system, using mathematical models.
  • In vitro: in vivo extrapolation using physiologically based pharmacokinetic models (IVIVE-PBPK).

Teaching and learning

The course emphasises the development of problem-solving skills. A large portion of the learning involves structured problems requiring you to apply theory and practical skills to solve typical problems that arise in drug development.

The following teaching and learning methods are used throughout the course:

  • taught lectures;
  • hands-on workshops;
  • self-directed learning to solve given problems;
  • webinars and tutorials by leading scientists in industry/academia;
  • supervised research;
  • mentorship in solving problems and writing the research dissertation;
  • independent study.

Coursework and assessment

We assess your achievement of the learning outcomes for this course through:

  • unit assignments (submitted electronically);
  • unit examinations;
  • research project dissertation and oral presentation.

Career opportunities

This course was originally developed for scientists working within the pharmaceutical industry who wished to qualify as modellers with hands-on experience. The qualification will enhance your abilities within your current role or provide you with skills to progress into new posts.

The course is also appropriate for science and engineering graduates who wish to enter the industry. The role of modelling and simulation or pharmacometrics is assuming greater and greater importance in the pharmaceutical industry.

Pharmaceutical companies and government regulatory agencies are recognising its value in making best use of laboratory and clinical data, guiding and expediting development and saving time and costs.

A range of well-paid jobs exist in this area across the globe. Scientific and industry publications often discuss the current shortage and growing need for modellers.



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