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High-level training in statistics and the modelling of random processes for applications in science, business or health care. Read more
High-level training in statistics and the modelling of random processes for applications in science, business or health care.

For many complex systems in nature and society, stochastics can be used to efficiently describe the randomness present in all these systems, thereby giving the data greater explanatory and predictive power. Examples include statistical mechanics, financial markets, mobile phone networks, and operations research problems. The Master’s specialisation in Applied Stochastics will train you to become a mathematician that can help both scientists and businessmen make better decisions, conclusions and predictions. You’ll be able to bring clarity to the accumulating information overload they receive.

The members of the Applied Stochastics group have ample experience with the pure mathematical side of stochastics. This area provides powerful techniques in functional analysis, partial differential equations, geometry of metric spaces and number theory, for example. The group also often gives advice to both their academic colleagues, and organisations outside of academia. They will therefore not only be able to teach you the theoretical basis you need to solve real world stochastics problems, but also to help you develop the communications skills and professional expertise to cooperate with people from outside of mathematics.

See the website http://www.ru.nl/masters/mathematics/stochastics

Why study Applied Stochastics at Radboud University?

- This specialisation focuses both on theoretical and applied topics. It’s your choice whether you want to specialise in pure theoretical research or perform an internship in a company setting.
- Mathematicians at Radboud University are expanding their knowledge of random graphs and networks, which can be applied in the ever-growing fields of distribution systems, mobile phone networks and social networks.
- In a unique and interesting collaboration with Radboudumc, stochastics students can help researchers at the hospital with very challenging statistical questions.
- Because the Netherlands is known for its expertise in the field of stochastics, it offers a great atmosphere to study this field. And with the existence of the Mastermath programme, you can follow the best mathematics courses in the country, regardless of the university that offers them.
- Teaching takes place in a stimulating, collegial setting with small groups. This ensures that you’ll get plenty of one-on-one time with your thesis supervisor at Radboud University .
- More than 85% of our graduates find a job or a gain a PhD position within a few months of graduating.

Career prospects

Master's programme in Mathematics

Mathematicians are needed in all industries, including the banking, technology and service industries, to name a few. A Master’s in Mathematics will show prospective employers that you have perseverance, patience and an eye for detail as well as a high level of analytical and problem-solving skills.

Job positions

The skills learned during your Master’s will help you find jobs even in areas where your specialised mathematical knowledge may initially not seem very relevant. This makes your job opportunities very broad and is the reason why many graduates of a Master’s in Mathematics find work very quickly.
Possible careers for mathematicians include:
- Researcher (at research centres or within corporations)
- Teacher (at all levels from middle school to university)
- Risk model validator
- Consultant
- ICT developer / software developer
- Policy maker
- Analyst

PhD positions

Radboud University annually has a few PhD positions for graduates of a Master’s in Mathematics. A substantial part of our students attain PhD positions, not just at Radboud University, but at universities all over the world.

Our research in this field

The research of members of the Applied Stochastics Department, focuses on combinatorics, (quantum) probability and mathematical statistics. Below, a small sample of the research our members pursue.

Eric Cator’s research has two main themes, probability and statistics.
1. In probability, he works on interacting particles systems, random polymers and last passage percolation. He has also recently begun working on epidemic models on finite graphs.
2. In statistics, he works on problems arising in mathematical statistics, for example in deconvolution problems, the CAR assumption and more recently on the local minimax property of least squares estimators.

Cator also works on more applied problems, usually in collaboration with people from outside statistics, for example on case reserving for insurance companies or airplane maintenance. He has a history of changing subjects: “I like to work on any problem that takes my fancy, so this description might be outdated very quickly!”

Hans Maassen researches quantum probability or non-commutative probability, which concerns a generalisation of probability theory that is broad enough to contain quantum mechanics. He takes part in the Geometry and Quantum Theory (GQT) research cluster of connected universities in the Netherlands. In collaboration with Burkhard Kümmerer he is also developing the theory of quantum Markov chains, their asymptotic completeness and ergodic theory, with applications to quantum optics. Their focal point is shifting towards quantum information and control theory, an area which is rapidly becoming relevant to experimental physicists.

Ross Kang conducts research in probabilistic and extremal combinatorics, with emphasis on graphs (which abstractly represent networks). He works in random graph theory (the study of stochastic models of networks) and often uses the probabilistic method. This involves applying probabilistic tools to shed light on extremes of large-scale behaviour in graphs and other combinatorial structures. He has focused a lot on graph colouring, an old and popular subject made famous by the Four Colour Theorem (erstwhile Conjecture).

See the website http://www.ru.nl/masters/mathematics/stochastics

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Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains. Read more
Technologies based on the intelligent use of data are leading to great changes in our everyday life. Data Science and Engineering refers to the know-how and competence required to effectively manage and analyse the massive amount of data available in a wide range of domains.

We offer a two-year Master of Science in Computer Science centered on this emerging field. The backbone of the program is constituted by three core units on advanced data management, machine learning, and high performance computing. Leveraging on the expertise of our faculty, the rest of the program is organised in four tracks, Business Intelligence, Health & Life Sciences, Pervasive Computing, and Visual Computing, each providing a solid grounding in data science and engineering as well as a firm grasp of the domain of interest.

By blending standard classes with recitations and lab sessions our program ensures that each student masters the theoretical foundations and acquires hands-on experience in each subject. In most units credit is obtained by working on a final project. Additional credit is also gained through short-term internship in the industry or in a research lab. The master thesis is worth 25% of the total credit.

TRACKS

• Business Intelligence. This track builds on first hand knowledge of business management and fundamentals of data warehousing, and focuses on data mining, graph analytics, information visualisation, and issues related to data protection and privacy.
• Health & Life Sciences. Starting from core knowledge of signal and image processing, bioinformatics and computational biology, this track covers methods for biomedical image reconstruction, computational neuroengineering, well-being technologies and data protection and privacy.
• Pervasive Computing. Security and ubiquitous computing set the scene for this track which deals with data semantics, large scale software engineering, graph analytics and data protection and privacy.
• Visual Computing. This track lays the basics of signal & image processing and of computer graphics & augmented reality, and covers human computer interaction, computational vision, data visualisation, and computer games.

PROSPECTIVE CAREER

Senior expert in Data Science and Engineering. You will be at the forefront of the high-tech job market since all big companies are investing on data driven approaches for decision making and planning. The Business Intelligence area is highly regarded by consulting companies and large enterprises, while the Health and Life Sciences track is mainly oriented toward biomedical industry and research institutes. Both the Pervasive and the Visual Computing tracks are close to the interests of software companies. For all tracks a job in a start-up company or a career on your own are always in order.

Senior computer scientist.. By personalizing your plan of study you can keep open all the highly qualified job options in software companies.

Further graduate studies.. In all cases, you will be fully qualified to pursue your graduate studies toward a PhD in Computer Science.

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The Department of Mathematics offers graduate courses leading to M.Sc., and eventually to Ph.D., degree in Mathematics. The Master of Science program aims to provide a sound foundation for the students who wish to pursue a research career in mathematics as well as other related areas. Read more
The Department of Mathematics offers graduate courses leading to M.Sc., and eventually to Ph.D., degree in Mathematics. The Master of Science program aims to provide a sound foundation for the students who wish to pursue a research career in mathematics as well as other related areas. The department emphasizes both pure and applied mathematics. Research in the department covers algebra, number theory, combinatorics, differential equations, functional analysis, abstract harmonic analysis, mathematical physics, stochastic analysis, biomathematics and topology.

Current faculty projects and research interests:

• Ring Theory and Module Theory, especially Krull dimension, torsion theories, and localization

• Algebraic Theory of Lattices, especially their dimensions (Krull, Goldie, Gabriel, etc.) with applications to Grothendieck categories and module categories equipped with torsion theories

• Field Theory, especially Galois Theory, Cogalois Theory, and Galois cohomology

• Algebraic Number Theory, especially rings of algebraic integers

• Iwasawa Theory of Galois representations and their deformations Euler and Kolyvagin systems, Equivariant Tamagawa Number
Conjecture

• Combinatorial design theory, in particular metamorphosis of designs, perfect hexagon triple systems

• Graph theory, in particular number of cycles in 2-factorizations of complete graphs

• Coding theory, especially relation of designs to codes

• Random graphs, in particular, random proximity catch graphs and digraphs

• Partial Differential Equations

• Nonlinear Problems of Mathematical Physics

• Dissipative Dynamical Systems

• Scattering of classical and quantum waves

• Wavelet analysis

• Molecular dynamics

• Banach algebras, especially the structure of the second Arens duals of Banach algebras

• Abstract Harmonic Analysis, especially the Fourier and Fourier-Stieltjes algebras associated to a locally compact group

• Geometry of Banach spaces, especially vector measures, spaces of vector valued continuous functions, fixed point theory, isomorphic properties of Banach spaces

• Differential geometric, topologic, and algebraic methods used in quantum mechanics

• Geometric phases and dynamical invariants

• Supersymmetry and its generalizations

• Pseudo-Hermitian quantum mechanics

• Quantum cosmology

• Numerical Linear Algebra

• Numerical Optimization

• Perturbation Theory of Eigenvalues

• Eigenvalue Optimization

• Mathematical finance

• Stochastic optimal control and dynamic programming

• Stochastic flows and random velocity fields

• Lyapunov exponents of flows

• Unicast and multicast data traffic in telecommunications

• Probabilistic Inference

• Inference on Random Graphs (with emphasis on modeling email and internet traffic and clustering analysis)

• Graph Theory (probabilistic investigation of graphs emerging from computational geometry)

• Statistics (analysis of spatial data and spatial point patterns with applications in epidemiology and ecology and statistical methods for medical data and image analysis)

• Classification and Pattern Recognition (with applications in mine field and face detection)

• Arithmetical Algebraic Geometry, Arakelov geometry, Mixed Tate motives

• p-adic methods in arithmetical algebraic geometry, Ramification theory of arithmetic varieties

• Topology of low-dimensional manifolds, in particular Lefschetz fibrations, symplectic and contact structures, Stein fillings

• Symplectic topology and geometry, Seiberg-Witten theory, Floer homology

• Foliation and Lamination Theory, Minimal Surfaces, and Hyperbolic Geometry

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This programme offers you the chance to study a range of modules in pure and applicable mathematics, giving you the opportunity to increase your knowledge and abilities in these areas. Read more
This programme offers you the chance to study a range of modules in pure and applicable mathematics, giving you the opportunity to increase your knowledge and abilities in these areas. Depending on your choices, you will take either 7 or 8 modules, allowing you to study several different topics in depth, and to focus on the areas that interest you most. Over 2 years you will also learn the methods of mathematical research: how to read mathematical papers and how to communicate mathematics, both in written form for your project dissertation, and orally when you give presentations about your project.

You will acquire the skills to pursue your interest in the subject, either formally with a research degree, or informally with independent reading. You will come to us as someone with a mathematics degree; you will graduate as a mathematician.

Why study this course at Birkbeck?

Offers modules in group theory, graph theory, combinatorics and applicable mathematics such as coding theory and cryptography.
Specially designed for part-time students: delivered via high-quality, face-to-face teaching in the evenings, so that you can fit study around daytime commitments.
You complete a project in your chosen area of mathematics, with guidance from an expert supervisor.
Birkbeck's mathematicians are all active researchers, mostly in the areas of algebra and combinatorics. We've developed this exciting course around those research strengths.
Birkbeck has a library and several workstation rooms. You can also use several local university libraries, including the collection of the London Mathematical Society, a 5-minute walk from Birkbeck's main building.

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Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business. Read more
Surrey Business School’s Business Analytics programme is dedicated to producing creative and knowledgeable Business Analysts with the ability to convert Big Data to actionable insight in business.

Whether it’s using Artificial Intelligence to improve a chess programme, or understanding the power of visualisation from a simple graph.

With input from industry experts in class and on-site, you will engage with real-world business problems.

PROGRAMME OVERVIEW

Artificial Intelligence and Machine Learning, Big Data. New technologies and ways of working are changing the way we make decisions.

This programme will take your career to the next level and develop your ability to confidently make high impact businesses decisions that are driven by data.

The programme focuses on two key areas: analysing business data, and solving business challenges analytically. Optional modules allow you to further specialise in areas such as the economic, managerial or finance or aspects of the subject.

Furthermore, you will benefit from hands-on experience of a wide range of analytics software such as simulators and mathematical tools.

PROGRAMME STRUCTURE

The programme is studied full-time over one academic year. 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 Analytics
-Supply Chain Analytics
-Econometrics I
-Machine Learning and Visualisations
-Principles of Accounting
-Foundations of Finance: Finance and Investments
-Supply Chain and Logistics Management
-Information for Decision Making
-Managing Decisions Implementation
-Introduction to Marketing Analytics
-Econometrics II
-Business Process Management
-Innovation Management
-Investment Analysis
-Dissertation

CAREER PROSPECTS

Business analytics students often pursue careers as consultants, researchers, managers, and analysts.

SOFTWARE

You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
-Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
-COGNOS and SQL Server for Business Intelligence for analytical processing
-Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
-SAP for Enterprise Resource Planning
-R, SPSS and EViews for coding, statistics and forecasting
-ILOG’s Optimisation Studio (Cplex) for optimisations
-Matlab for algorithms and programming and Simulink (SimEvents) for simulations
-Arena (or Simul8) for Discrete Event Simulations

EDUCATIONAL AIMS OF THE PROGRAMME

The programme’s aim is to provide a high quality education that is both intellectually rigorous and at the forefront of management science research, relevant for problem solving and decision making by managers.

It will respond to the emergent needs of corporations and academia for professionals who are able to work with analytical tools to generate value from available Information depots and take advantage of the vast amounts of data now provided by the modern ICT and ERP systems, which underlie the operations of modern corporations.

The program will implant understanding of the theoretical base around knowledge management and knowledge work, practical skills and experience in using analytical software tools.

It will allow future professional managers and consultants to cope with an increasingly complex and global operational environment of the modern corporation.

Completion of the programme will provide a sound foundation for those considering continuing their academic development towards a PhD degree in the management disciplines.

The programme is structured in a way that would provide students with a choice between a more quantitative intensive track of modules or a qualitative analytic (business development track) which would reflect students’ personal strengths and preferences and match future career aspirations.

The compulsory modules provide a sound foundation which builds an analytical skillset using relevant statistical and management theories, and supports the development of practical hands-on experience applying the theoretical aspects using real-world data to address corporate challenges and find solutions to actual problems.

The readings in the module will build a sound basis which would allow students to access and understand the academic literature and undertake empirical investigations in the areas of decision modelling and business development.

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:

Knowledge and understanding
-A systematic, in-depth understanding of the development; issues and influences relevant to discipline of Management Decision Making, Management Science, and Data Science.
-Deep and thorough understanding of quantitative analytical methodologies and hands-on experience with decision-making software and data management tools.
-Knowledge about issues, application and analysis of Big Data
-An understanding of the academic research process.

Intellectual / cognitive skills
-Demonstrate deep learning, understanding of the material and ability to apply the knowledge and demonstrate skills in problem solving in the topic space of the modules studied
-Carry out assessments of data in a repository, select the appropriate analysis tools, design and execute an analytical methodology (not required for PG Certificate), apply adequate visualization methodologies to present the results and interpret the findings and finally to communicate the results effectively to a select audience

Professional practical skills
-Demonstrate the ability to independently evaluate critical approaches and techniques relevant to Business Analytics
-Know and apply a range of techniques and tools to analyse data related to business operations
-Capability of selecting the right methodology and software to solve management and operational business issues
-Relate existing knowledge structures and methodologies to analytical business challenges

Key / transferable skills
-Conduct critical literature review; to select, define and focus upon an issue at an appropriate level
-Develop and apply relevant and sound methodology
-Apply the methodology to analyse the issue
-Develop logical conclusions and recommendations
-Be aware of the limitations of the research
-Identify modifications to existing knowledge structures and theoretical frameworks and therefore to prose new areas for investigation, new problems, new or alternative applications or methodological applications

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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Mathematics is at the heart of advances in science, engineering and technology, as well as being an indispensable problem-solving and decision-making tool in many other areas of life. Read more
Mathematics is at the heart of advances in science, engineering and technology, as well as being an indispensable problem-solving and decision-making tool in many other areas of life. This MSc course enables you to delve deeply into particular aspects of pure and applied mathematics, through a wide choice of modules in fascinating areas such as fractal geometry, coding theory and analytic theory. You’ll complete your MSc with a piece of independent study, exploring the history of modern geometry, advances in approximation theory, variational methods applied to eigenvalue problems, or algebraic graph theory and culminating in a dissertation on the topic of your choice.

Key features of the course

•Ideal for mathematically inclined scientists and engineers as well as mathematicians
•Extends your knowledge and refines your abilities to process information accurately, and critically analyse and communicate complex ideas
•Develops an enhanced skill set that will put you at an advantage in careers as diverse as mathematics, education, computer science, economics, engineering and finance.
•The most popular MSc in mathematics in the UK.
This qualification is eligible for a Postgraduate Loan available from Student Finance England. For more information, see Fees and funding

Course details

You can take a number of different routes towards your qualification - see the full module list for all options.

Modules

The modules in this qualification are categorised as entry, intermediate and dissertation. Check our website for start dates as some modules are not available for study every year.

Entry:

• Calculus of variations and advanced calculus (M820)
• Analytic number theory I (M823)

Intermediate:

• Nonlinear ordinary differential equations (M821)
• Applied complex variables (M828) - next available in October 2017 and following alternate years
• Analytic number theory II (M829) - next available in October 2018 and following alternate years
• Approximation theory (M832) - next available in October 2018 and following alternate years
• Advanced mathematical methods (M833) - next available in October 2017 and following alternate years
• Fractal geometry (M835) - next available in October 2017 and following alternate years
• Coding theory (M836) - next available in October 2018 and following alternate years
• Dissertation: Dissertation in mathematics (M840)

Module study order:

•You must normally pass at least one entry level module before studying an intermediate module.
•You must pass Analytic number theory I (M823) before studying Analytic number theory II (M829).
•You must normally pass four modules before studying the Dissertation in mathematics (M840).
•Some topics for the dissertation have prerequisite modules

Otherwise within each category modules may be studied in any order, and you may register for a module while studying a pre-requisite for that module (i.e. before you know whether you have passed the pre-requisite module or not).

To gain this qualification, you need 180 credits as follows:

150 credits from this list:

Optional modules

• Advanced mathematical methods (M833)
• Analytic number theory I (M823)
• Analytic number theory II (M829)
• Applied complex variables (M828)
• Approximation theory (M832)
• Calculus of variations and advanced calculus (M820)
• Coding theory (M836)
• Fractal geometry (M835)
• Nonlinear ordinary differential equations (M821)

Plus

Compulsory module

Dissertation in mathematics (M840)

The modules quoted in this description are currently available for study. However, as we review the curriculum on a regular basis, the exact selection may change over time.

Credit transfer

For this qualification, we do not allow you to count credit for study you have already done elsewhere.

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What do Facebook, the financial system, Internet or the brain have in common?. All are connected in a network. Read more
What do Facebook, the financial system, Internet or the brain have in common?

All are connected in a network. From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc in Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), public health (epidemic spreading models), marketing and IT (social media, data analytics) to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students whose undergraduate degree is in mathematics or a cognate discipline who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

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You will gain deep knowledge of a chosen topic in mathematics or statistics and develop your research skills in project planning, reviewing literature, group discussions, research presentations and writing publications. Read more
You will gain deep knowledge of a chosen topic in mathematics or statistics and develop your research skills in project planning, reviewing literature, group discussions, research presentations and writing publications.

You can choose to work with experts from a range of areas including quantum cryptography, graph theory, statistical analysis, bioinformatics and mathematical modelling.

You will take three taught modules each providing you with the underpinning theory to support your research work.

What will I study?

You will undertake a year-long research project, one compulsory module on Research Methods and then choose any two from the options below:

Options in Statistics

:
-Computational Statistics and Data Analysis
-Applied Statistics
-Statistical Modelling

[[ Options in Applied Mathematics]]:
- Mathematical Recipes
- Topics in Mathematical Biology
- Linear Systems
- Topics in Applied Mathematics

Options in Pure Mathematics

- Topics in Pure Mathematics
- Coding Theory and Cryptography

COME VISIT US ON OUR NEXT OPEN DAY!

Register here: https://www.ntu.ac.uk/university-life-and-nottingham/open-days/find-your-open-day/science-and-technology-postgraduate-and-professional-open-event2.

The course is a part of the School of Science and Technology (http://www.ntu.ac.uk/sat) which has first-class facilities (http://www.ntu.ac.uk/sat/facilities).

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What do Facebook, the financial system, Internet or the brain have in common?. "Everything is connected, all is network". Read more
What do Facebook, the financial system, Internet or the brain have in common?

"Everything is connected, all is network"
From the underlying skeleton of social relations, the interdependent evolution of our financial system, to the emergent collective computation in the brain, most of the complex systems that appear in society, technology, and nature are ultimately characterised by a nontrivial pattern of inter-relations. This underlying architecture is in turn shaping how information diffuses and spreads, how resilient the system is against attacks or perturbations, or how complex patterns emerge at the systemic level from the aggregation of seemingly simple individuals.

Our MSc Network Science will provide a thorough grounding in the core principles of modelling and analysis of complex and networked systems, along with the principal analytical and numerical methodologies. This will open to students a host of career opportunities in systems and networks modelling industries, spanning the IT, financial, and biomedical sectors, that are now requiring such specialist knowledge and skills.

Network Science is a very active and rapidly evolving research field with high societal impact, which stands at the crossroads of graph theory, complexity and data analysis. Addressing the description and modelling of the architecture and dynamics of complex systems -systems composed by many interacting units that show collective behaviour- it stands as a new kind of science to cope with some of the most challenging endeavours we face today, in an ever increasingly more connected society.
Its impact and applications outside academia pervades technological sectors such as communications and infrastructures (Internet, transportation networks, energy networks, urban mobility), finance (financial risk and systemic instability, financial networks, interbank cross-correlations), marketing and IT (social media, data analytics), public health (epidemic spreading models), or biostatistics and network biology (brain modelling, protein interaction networks, postgenomic era), to cite a few. This specialist masters programme aims at providing graduate students and professionals with a rigorous training in the underlying mathematical concepts, the analysis and modelling of complex networks and networked systems, complemented with training in computing, numerical simulations and massive data analysis. It is aimed towards students with a mathematical background who wish to enter a career involving analysis and optimisation of diverse kinds of networks, networked dynamics and models.

Why study your MSc Network Science at Queen Mary?
This is a pioneering MSc in the UK, a joint programme, taught by our Schools of Mathematical Sciences, and Electronic Engineering and Computer Science, drawing on their strengths in research and teaching in the area of complex networks, mathematical modelling of complex systems, and data mining.

We teach what we know and what we do best. Within the School of Mathematics, the Complex Systems & Networks group is one of the biggest hubs in Network Science within the UK, where we address both fundamental and applied challenges in the mathematical modelling of complex systems with clear societal impact, in collaboration with several industrial stakeholders. Within the School of Electronic Engineering, the Networks group was founded in 1987, and has hugely expanded ever since, bringing their expertise in online social networks, data mining and cloud computing. The coalescence of both groups expertises has fostered the creation of this unique MSc.

More about our two schools

Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research, teaching excellence and unrivalled links with business and the public sector. The School of Mathematical Sciences has a distinguished history on itself. We have been conducting pioneering mathematical research since the 1950s, and as one of the largest mathematical departments in the UK, with over 50 members of staff, the school can offer diverse postgraduate study opportunities across the field, from pure and applied mathematics, to finance and statistics. Along with the MSc in Network Science, our cohort of postgraduate students specialise in Mathematics and Statistics, Mathematical Finance and Financial Computing. We are one of the UK’s leading universities in the most recent national assessment of research quality, we were placed ninth in the UK (REF 2014) amongst multi-faculty universities. This means that the teaching on our postgraduate programmes is directly inspired by the world-leading research of our academics. Our staff includes international leaders in many areas of mathematical research, and the School is a hive of activity, providing a vibrant intellectual space for postgraduate study.

The School of Electronic Engineering and Computer Science is internationally recognised for their pioneering and ground-breaking research in several areas including machine learning and applied network analysis. This expertise uniquely complements the more theoretical knowledge offered by the School of Mathematical Sciences, providing a well balanced mix of theory and applications and offering a deep and robust programme that combines the foundations of the mathematics of networks with the latest cutting edge applications in real world problems.

Additionally, Queen Mary holds a university-level Bronze Award for the Athena SWAN Charter, which recognises and celebrates good employment practice for women working in mathematics, science, engineering and technology in higher education and research.

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Businesses, organisations, and individuals all strive to work as effectively as possible. Operational research uses advanced statistical and analytical methods to help improve the complex decision-making processes to deliver a product or service. Read more
Businesses, organisations, and individuals all strive to work as effectively as possible. Operational research uses advanced statistical and analytical methods to help improve the complex decision-making processes to deliver a product or service. Working in this field, you might be identifying future needs for a business, evaluating the time-life value of a customer, or carrying out computer simulations for airlines.

Our MSc Statistics and Operational Research will appeal if your first degree included mathematics as its major subject, and we expect you to have prior knowledge of statistics – for example significance testing or basic statistical distributions – and operational research such as linear programming.

You specialise in areas including:
-Continuous and discrete optimisation
-Time series econometrics
-Heuristic computation
-Experimental design
-Machine learning
-Linear models

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

This course can also be studied to a PGDip level - for more information, please view this web-page: http://www.essex.ac.uk/courses/details.aspx?mastercourse=PG00808&subgroup=2

Our expert staff

Our Department of Mathematical is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

Our MSc Statistics and Operational Research will equip you with employability skills like problem solving, analytical reasoning, data analysis, and mathematical modelling, as well as training you in independent work, presentation and writing skills.

Your exposure to current active research areas, such as decomposition algorithms on our module, Combinatorial Optimisation, prepares you for further study at doctoral level. Graduates of this course now hold key positions in government, business and academia.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Nonlinear Programming
-Combinatorial Optimisation
-Modelling Experimental Data (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)
-Applied Statistics (optional)
-Bayesian Computational Statistics
-Research Methods
-Dissertation
-Ordinary Differential Equations (optional)
-Graph Theory (optional)
-Partial Differential Equations (optional)
-Portfolio Management (optional)
-Machine Learning and Data Mining (optional)
-Evolutionary Computation and Genetic Programming (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)
-Applications of Data Analysis (optional)
-Mathematical Research Techniques Using Matlab (optional)

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Mathematics is the language that underpins the rest of science. Our Department of Mathematical Sciences has an international reputation in many areas like such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology. Read more
Mathematics is the language that underpins the rest of science. Our Department of Mathematical Sciences has an international reputation in many areas like such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

Graduate Diplomas last for six to nine months (full-time) and include the modules and assessed work of a Masters, without a dissertation. Our Graduate Diploma in Mathematics gives you training in basic mathematics techniques if your first degree contained only a modest amount of mathematics, so that you can proceed to a Masters in mathematics.

At Essex, Mathematics has truly broad reach; we are working on projects ranging from the economic impact of the behaviour of dairy cows, to understanding crowd behaviour through modelling a zombie apocalypse, to circular Sudoku and other puzzles. Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

You therefore gain an exceptional range of knowledge and skills that are currently in demand in mathematically oriented employment; in business, commerce, industry, government service, education and in the wider economy.

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Applied Statistics (optional)
-Bayesian Computational Statistics (optional)
-Combinatorial Optimisation (optional)
-Complex Variables and Applications (optional)
-Contingencies I
-Contingencies II
-Cryptography and Codes
-Finance and Financial Reporting (optional)
-Financial Derivatives (optional)
-Graph Theory (optional)
-Introduction to Numerical Methods (optional)
-Linear Algebra (optional)
-Mathematical Biology (optional)
-Mathematical Methods (optional)
-Mathematics of Portfolios (optional)
-Modelling Experimental Data (optional)
-Nonlinear Programming (optional)
-Ordinary Differential Equations (optional)
-Partial Differential Equations (optional)
-Project: Mathematics (optional)
-Quantum Mechanics (optional)
-Real Analysis (optional)
-Statistical Methods (optional)
-Statistics II (optional)
-Stochastic Processes (optional)
-Survival Analysis (optional)
-The Laws of Physics (optional)
-Vector Calculus (optional)

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This course is for people who need to strengthen their maths knowledge fairly quickly before starting their training to become a secondary mathematics teacher. Read more
This course is for people who need to strengthen their maths knowledge fairly quickly before starting their training to become a secondary mathematics teacher.

Course overview

This 24-week fast track course will boost your maths knowledge so that you are able to teach maths up to A Level. To join the Subject Knowledge Enhancement (SKE) course, you should already have received a conditional offer for initial teacher training – or you should at least be considering an application for it.

Typically, you should have already received a conditional offer for initial teacher training (ITT) – or you should at least be considering an application for it. Our course ends in July so that you are ready for ITT in September.

For Home/EU students, there is no tuition fee and you will be paid a bursary while you are studying on the course.

You will cover areas such as geometry, calculus, statistics, mechanics, patterns and equations as well as common errors and misconceptions in mathematics. At the initial interview, we will advise you whether this 24-week fast track course is suitable for you or whether an alternative course would be better for your circumstances.

Once you have successfully completed the SKE course and your initial teacher training, your career prospects will be very good. There is a national shortage of mathematics teachers and there are excellent job prospects, both in the North East and throughout the country.

Reports on our provision by external examiners have been very positive. Comments include: “It is clear that the course remains very successful in preparing candidates appropriately for their subsequent teacher training course”.

Course content

This course enhances your skills and knowledge in mathematics, preparing you for teacher training. You will study the following units:
-Patterns and Equations
-Structure and Pattern
-Development of Geometric Thinking
-Nature of Mathematics
-Investigating Calculus
-Enriching and Strengthening Mathematics
-Elementary Geometry
-Development of Mathematics
-Statistics
-Mechanics
-Using LOGO and 3D Geometry
-Errors and Misconceptions in Mathematics
-Matrices
-Investigating Sequences and Series

Teaching and assessment

We use a wide variety of teaching and learning methods that include lectures, independent work, directed tasks, written assignments and use of ICT. In addition to attending taught sessions, we encourage you to undertake some voluntary work experience in a school. Assessment methods include written work, exams and presentations. We assess all units.

Facilities & location

This course is based on the banks of the River Wear at The Sir Tom Cowie Campus at St Peter’s. Interactive whiteboards are available in our classrooms and we encourage you to use mathematical software, such as Autograph graph-plotting software, to support your learning.

When it comes to IT provision you can take your pick from over a hundred PCs in the St Peter’s Library, a dedicated computer classroom, and wireless access zones. If you have any problems, just ask the friendly helpdesk team.

The University of Sunderland is a vibrant learning environment with a strong international dimension thanks to the presence of students from around the world.

Employment & careers

This course enhances your maths subject knowledge, allowing you to progress on to a teacher training programme such as the University of Sunderland’s PGCE Mathematics Secondary Education and then achieve Qualified Teacher Status.

There is a shortage of mathematics teachers in England so there are excellent career opportunities both in the North East and nationwide.

The starting salary of a Newly Qualified Teacher is over £22,000, with extra if you work in London. Teachers can expect to see their salaries rise by an average of 30 per cent after their first four years in the job.

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This course is for people who need to strengthen their maths knowledge before starting their training to become a secondary mathematics teacher. Read more
This course is for people who need to strengthen their maths knowledge before starting their training to become a secondary mathematics teacher.

Course overview

Do you need to enhance your mathematical understanding in order to achieve the expertise that is required to teach maths up to A Level? At Sunderland we provide the in-depth knowledge that you need. This course is the standard 28-week course rather than the faster track 24-week Mathematics SKE course.

Typically, you should have already received a conditional offer for initial teacher training (ITT) – or you should at least be considering an application for it. Our course ends in June so that you are ready for ITT in September.

For Home/EU students, there is no tuition fee, and you will be paid a bursary while you are studying on the course.

You will cover areas such as geometry, calculus, statistics, mechanics, patterns and equations as well as common errors and misconceptions in mathematics. At the initial interview, we will advise you whether this 28-week course is suitable for you or whether an alternative course would be better for your circumstances.

When you successfully complete your ITT, your career prospects will be very good. There is a national shortage of mathematics teachers and there are excellent job prospects.

If you are not sure whether to undertake the 28-week or 24-week course, we will advise you when you come to interview.

A recent external examiner noted, after discussions with students, that: “their enthusiasm for the course was evident, and they were able to describe how it had both aided their mathematical development and helped them to think about what good teaching is and how they can become good teachers”.

Course content

This course enhances your skills and knowledge in mathematics, preparing you for teacher training. You will study the following units:
-Structure and Pattern 1
-Structure and Pattern 2
-Using and Applying ICT in Mathematics 1
-Using and Applying ICT in Mathematics 2
-The Development of Geometric Thinking
-The Development of Mathematics
-Statistics in Action
-Mechanics in Action
-The Nature of Mathematics
-Errors and Misconceptions in Mathematics

Teaching and assessment

We use a wide variety of teaching and learning methods that include lectures, independent work, directed tasks, written assignments and use of ICT. In addition to attending taught sessions, you will be required to undertake some voluntary work experience in a school. Assessment methods include written work, exams and presentations. We assess all units.

Facilities & location

This course is based on the banks of the River Wear at The Sir Tom Cowie Campus at St Peter’s. Interactive whiteboards are available in our classrooms and we encourage you to use mathematical software, such as Autograph graph-plotting software, to support your learning.

When it comes to IT provision you can take your pick from over a hundred PCs in the St Peter’s Library, a dedicated computer classroom, and wireless access zones. If you have any problems, just ask the friendly helpdesk team.

The University of Sunderland is a vibrant learning environment with a strong international dimension thanks to the presence of students from around the world.

Employment & careers

This course enhances your maths subject knowledge, allowing you to progress on to a teacher training programme such as the University of Sunderland's PGCE Mathematics Secondary Education and then achieve Qualified Teacher Status.

There is a shortage of mathematics teachers in England, so there are excellent career opportunities both in the North East and nationwide.

The starting salary of a Newly Qualified Teacher is over £22,000, with extra if you work in London. Teachers are likely to see their salaries rise by an average of 30 per cent after their first four years in the job.

Read less
Joining the Department as a postgraduate is certainly a good move. The Department maintains strong research in both pure and applied mathematics, as well as the traditional core of a mathematics department. Read more
Joining the Department as a postgraduate is certainly a good move. The Department maintains strong research in both pure and applied mathematics, as well as the traditional core of a mathematics department. What makes our Department different is the equally strong research in fluid mechanics, scientific computation and statistics.

The quality of research at the postgraduate level is reflected in the scholarly achievements of faculty members, many of whom are recognized as leading authorities in their fields. Research programs often involve collaboration with scholars at an international level, especially in the European, North American and Chinese universities. Renowned academics also take part in the Department's regular colloquia and seminars. The faculty comprises several groups: Pure Mathematics, Applied Mathematics, Probability and Statistics.

Mathematics permeates almost every discipline of science and technology. We believe our comprehensive approach enables inspiring interaction among different faculty members and helps generate new mathematical tools to meet the scientific and technological challenges facing our fast-changing world.

The MPhil program seeks to strengthen students' general background in mathematics and mathematical sciences, and to expose students to the environment and scope of mathematical research. Submission and successful defense of a thesis based on original research are required.

Research Foci

Algebra and Number Theory
The theory of Lie groups, Lie algebras and their representations play an important role in many of the recent development in mathematics and in the interaction of mathematics with physics. Our research includes representation theory of reductive groups, Kac-Moody algebras, quantum groups, and conformal field theory. Number theory has a long and distinguished history, and the concepts and problems relating to the theory have been instrumental in the foundation of a large part of mathematics. Number theory has flourished in recent years, as made evident by the proof of Fermat's Last Theorem. Our research specializes in automorphic forms.

Analysis and Differential Equations
The analysis of real and complex functions plays a fundamental role in mathematics. This is a classical yet still vibrant subject that has a wide range of applications. Differential equations are used to describe many scientific, engineering and economic problems. The theoretical and numerical study of such equations is crucial in understanding and solving problems. Our research areas include complex analysis, exponential asymptotics, functional analysis, nonlinear equations and dynamical systems, and integrable systems.

Geometry and Topology
Geometry and topology provide an essential language describing all kinds of structures in Nature. The subject has been vastly enriched by close interaction with other mathematical fields and with fields of science such as physics, astronomy and mechanics. The result has led to great advances in the subject, as highlighted by the proof of the Poincaré conjecture. Active research areas in the Department include algebraic geometry, differential geometry, low-dimensional topology, equivariant topology, combinatorial topology, and geometrical structures in mathematical physics.

Numerical Analysis
The focus is on the development of advance algorithms and efficient computational schemes. Current research areas include: parallel algorithms, heterogeneous network computing, graph theory, image processing, computational fluid dynamics, singular problems, adaptive grid method, rarefied flow simulations.

Applied Sciences
The applications of mathematics to interdisciplinary science areas include: material science, multiscale modeling, mutliphase flows, evolutionary genetics, environmental science, numerical weather prediction, ocean and coastal modeling, astrophysics and space science.

Probability and Statistics
Statistics, the science of collecting, analyzing, interpreting, and presenting data, is an essential tool in a wide variety of academic disciplines as well as for business, government, medicine and industry. Our research is conducted in four categories. Time Series and Dependent Data: inference from nonstationarity, nonlinearity, long-memory behavior, and continuous time models. Resampling Methodology: block bootstrap, bootstrap for censored data, and Edgeworth and saddle point approximations. Stochastic Processes and Stochastic Analysis: filtering, diffusion and Markov processes, and stochastic approximation and control. Survival Analysis: survival function and errors in variables for general linear models. Probability current research includes limit theory.

Financial Mathematics
This is one of the fastest growing research fields in applied mathematics. International banking and financial firms around the globe are hiring science PhDs who can use advanced analytical and numerical techniques to price financial derivatives and manage portfolio risks. The trend has been accelerating in recent years on numerous fronts, driven both by substantial theoretical advances as well as by a practical need in the industry to develop effective methods to price and hedge increasingly complex financial instruments. Current research areas include pricing models for exotic options, the development of pricing algorithms for complex financial derivatives, credit derivatives, risk management, stochastic analysis of interest rates and related models.

Facilities

The Department enjoys a range of up-to-date facilities and equipment for teaching and research purposes. It has two computer laboratories and a Math Support Center equipped with 100 desktop computers for undergraduate and postgraduate students. The Department also provides an electronic homework system and a storage cloud system to enhance teaching and learning.

To assist computations that require a large amount of processing power in the research area of scientific computation, a High Performance Computing (HPC) laboratory equipped with more than 200 high-speed workstations and servers has been set up. With advanced parallel computing technologies, these powerful computers are capable of delivering 17.2 TFLOPS processing power to solve computationally intensive problems in our innovative research projects. Such equipment helps our faculty and postgraduate students to stay at the forefront of their fields. Research projects in areas such as astrophysics, computational fluid dynamics, financial mathematics, mathematical modeling and simulation in materials science, molecular simulation, numerical ocean modeling, numerical weather prediction and numerical methods for micromagnetics simulations all benefit from our powerful computing facilities.

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Make your input really count as a postgraduate student in the Department of Computer Science and Engineering. Traditional computer science research covers the hardware and software of computer systems and their applications. Read more
Make your input really count as a postgraduate student in the Department of Computer Science and Engineering. Traditional computer science research covers the hardware and software of computer systems and their applications. Computer science programs at HKUST emphasize an integrated approach to the study of computers and computing methods to collect, process, analyze and transmit information to support relevant and useful applications in modern life.

The Department's goal is to offer a full range of postgraduate courses and research projects to meet the needs and interests of our students and to help solve relevant problems for society. Our world-class faculty members engage in cutting-edge research at the heart of the information technology revolution and our postgraduate students are involved in both applied and fundamental research. The Department has 50 full-time faculty members and 180 postgraduate students.

Computer science is still a young field. The world is only just beginning to realize the potential of information technology. The Department and its programs prepare students to meet the exciting challenges that await and to generate new advances in computing that will fuel future progress.

The MPhil program seeks to strengthen students' knowledge in computer science and expose them to issues involved in the development, scientific, educational and commercial applications of computer systems. Students are required to undertake coursework and successfully complete a thesis to demonstrate competence in research.

Research Foci

The Department's research involves many different areas:
Artificial Intelligence
Machine learning, data mining and pattern recognition, knowledge representation and reasoning, robotics and sensor-based activity recognition, multi-agent and game theory, and speech and language processing.

Data, Knowledge and Information Management
Large-scale data management, modeling and distribution encompassing web query processing, information retrieval and web search, data mining, enterprise systems, high-performance data management systems on modern computers, and database support for science applications.

Human-Computer Interaction
Augmented reality, multi-touch interaction, crowdsourcing, multimodal communication, affective computer, visual analytics of big data, intelligent interface for robots, E-learning, healthcare and e-commerce.

Networking and Computer Systems
Pervasive computing and sensor networks, peer-to-peer computing, grid computing, high-performance switches and routers, video delivery and multicasting, multimedia networking, MAC protocols for ad-hoc networks, web cache management, DDOS detection and defense, and resource management and allocation in optical networks.

Software Technology and Applications
Software engineering, data mining for software analysis and debugging, computer music, cryptography and security, internet computing.

Theoretical Computer Science
Combinatorial optimization, performance analysis techniques, computational geometry, formal languages and machines, graph algorithms, and algorithmic combinatorial game theory.

Vision and Graphics
Computer vision, computer graphics, medical image analysis, biometric systems, and video processing.

Facilities

The Department has excellent facilities to support its programs and is committed to keeping its computing facilities up to date. There are about 700 workstations and PCs, including those in four teaching laboratories, three MS Windows Labs and one Linux lab. The Department also runs several research laboratories with specific facilities, including the computer engineering, database, Human-Computer Interaction Initiative, vision and graphics labs. Specialized project laboratories include:
-The HCI lab, has a 360 degree circular CCD-camera capturing system with a 4x3 large display array and 120" rear projected 3D active stereo system, and large-sized multi-touch panels, linked with various physiological sensors for gesture/body tracking;
-The Human Language Technology Center, with various corpora and a Linux cluster;
-The System and Media Laboratory, partially funded by Deutsche Telekom, focusing mobile computing and any interesting topics related to social network; and
-The Networking group, that maintains different sets of network cluster for Data center and cloud computing research.
-Different research groups maintain their own CPU/GPU cluster customized for different research need.

In addition, the Department manages a pool of Linux servers as CPU/GPU cluster for general research projects demanding significant system resources, and acquires a GPU cluster for the whole University. The file servers are connected with one HDS AMS2100 and one HDS HUS110 Storage Area Network (SAN), with a total capacity of more than 60TB. There is also a pool of high performance servers with GPUs dedicated for undergraduate courses on parallel computing and Big Data analysis.

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