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Masters Degrees (Optimization)

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This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets. Read more

This programme gives you a flexible syllabus to suit the demands of employers that use modern financial tools and optimization techniques in areas such as the financial sector and energy markets.

We will give you sound knowledge in financial derivative pricing, portfolio optimization and financial risk management.

We will also provide you with the skills to solve some of today’s financial problems, which have themselves been caused by modern financial instruments. This expertise includes modern probability theory, applied statistics, stochastic analysis and optimization.

Adding depth to your learning, our work placement programme puts you at the heart of financial organisations such as Moody's Analytics, Standard Life Investment and Lloyds Banking Group.

Programme structure

This programme involves two taught semesters of compulsory and option courses, followed by a dissertation project. You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take a number of compulsory courses and optional courses. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project worth 60 credits, possibly with one of our industry partners, for the award of the MSc degree.

Compulsory courses:

  • Discrete-Time Finance (10 credits, S1)
  • Stochastic Analysis in Finance (20 credits, S1)
  • Fundamentals of Optimization (10 credits, S1)
  • Research-Linked Topics (10 credits, full-year)
  • Finance, Risk and Uncertainty (10 credits, S1)
  • Risk-Neutral Asset Pricing (10 credits, S2)
  • Simulation (10 points, S2)
  • Optimization Methods in Finance (10 credits, S2)

Optional courses:

  1. Operations Research and Mathematical Finance courses:
  • Financial Risk Theory (10 credits, S1)
  • Computing for Operational Research and Finance (10 credits, S1)
  • Fundamentals of Operational Research (10 credits, S1)
  • Stochastic Control and Dynamic Asset Allocation (10 credits, S2)
  • Credit Scoring (10 credits, S2)
  • Financial Risk Management (10 credits, S2)
  • Risk Analysis (5 credits, S2)
  • Stochastic Modelling (10 credits, S2)
  1. Relevant Statistical and Numerical courses:
  • Multivariate Data Analysis (10 credits, S2)
  • Numerical Partial Differential Equations (10 credits, S2)
  • Advanced Time Series Econometrics (10 credits, S2) (offered by the School of Economics)
  1. Programming courses:
  • Object-Oriented programming with applications (10 credits, S1)
  • Parallel Numerical Algorithms (10 credits, S1), (offered by EPCC)
  • Programming Skills (10 credits, S1), (offered by EPCC)
  1. Optimization courses:
  • Combinatorial Optimization (5 credits, S2)
  • Large Scale Optimization for Data Science (10 credits, S2)
  • Modern Optimization Methods for Big Data Problems (10 credits, S2)
  • Nonlinear Optimization (10 credits, S2)
  • Stochastic Optimization (5 credits, S2)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates have gone on to work in major financial institutions or to continue their studies by joining PhD programmes.



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This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights and routing mobile phone calls to managing investments and minimising risks. Read more

This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights and routing mobile phone calls to managing investments and minimising risks. Operational Research (OR) is an important skill that is in high demand.

This MSc will give an Operational Research perspective on computational optimization.

Programme structure

This programme involves two taught semesters of compulsory and option courses followed by your dissertation project.

Courses previously offered include:

Compulsory courses:

  • Fundamentals of Optimization
  • Fundamentals of Operational Research
  • Methodology, Modelling and Consulting Skills
  • Simulation
  • Stochastic Modelling

Plus two courses from:

  • Statistical Methodology
  • Probability and Statistics
  • Object-Oriented Programming with Applications
  • Statistical Programming
  • Scientific Computing
  • Python Programming

As part of your option course choices, Operational Research with Computational Optimization requires you to study two from a set of courses which, previously, has included:

  • Large Scale Optimization for Data Science
  • Integer and Combinatorial Optimization
  • Topics in Applied Optimization.

Career opportunities

The skills you will learn are in demand by a vast range of high-profile organisations including consultancy firms, companies with operational research departments such as airlines or telecommunications providers, financial firms and the public sector.

Recent graduates have joined British Airways, the Government OR Service, Barclays, Deloitte, Capgemini and smaller specialised OR, finance and energy companies.

Industry-based dissertation projects

The dissertation projects of approximately half the students on this programme take place in public and private sector organisations. Other students choose a University-based project.



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This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights, handling large data sets to managing investments and minimizing risks. Read more

This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights, handling large data sets to managing investments and minimizing risks. The skills of Operational Research (OR) and Data Science are in high demand.

The MSc in Operational Research with Data Science is a new, forward-looking programme that delivers high-quality training in operational research, optimization and statistics. Students will have strong technical skills in these areas and the ability to apply them using appropriate software.

This MSc programme delivers:

  • technical skills in operational research, optimization and statistics
  • practical skills in programming and modelling for a wide range of applications
  • communications skills in writing and audio-visual presentation

Programme structure

You need to obtain a total of 180 credits to be awarded the MSc. All students take courses during semester 1 and 2 to the value of 120 credits. Successful performance in these courses (assessed through coursework or examinations or both) permits you to start work on a three-month dissertation project (60 credits) for the award of the MSc degree.

Compulsory courses have previously included:

  • Fundamentals of Optimization
  • Fundamentals of Operational Research
  • Methodology, Modelling and Consulting Skills

Themed courses have previously included:

  • Introductory Applied Machine Learning
  • Bioinformatics 1
  • Machine Learning and Pattern Recognition
  • Statistical Methodology
  • Simulation
  • Object-Oriented Programming with Applications
  • Statistical Programming
  • Scientific Computing

Learning outcomes

At the end of this programme you will have:

  • flexible problem-solving skills based on deep knowledge of operational research, optimization, data analysis techniques and the ability to apply them using appropriate software
  • transferable skills to maximize their prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management

Career opportunities

Graduates will gain the transferable skills required to pursue careers in a data-rich operational research environment, and will be in an ideal position to apply for work in a wide range of institutions in the public and private sector. The degree is also excellent preparation for further study in operational research, optimization or data science.

Industry-based dissertation projects

The dissertation projects of approximately half the students on this programme take place in public and private sector organisations. Other students choose a University-based project.



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This programme is both technical and pragmatic. You will acquire the ability to integrate state-of-the-art knowledge of statistics and optimisation to address, analyse and provide a rational appraisal of a given problem in different professional contexts. Read more
This programme is both technical and pragmatic. You will acquire the ability to integrate state-of-the-art knowledge of statistics and optimisation to address, analyse and provide a rational appraisal of a given problem in different professional contexts. This is a multidisciplinary field that involves the study of mathematical optimisation techniques, operational research methods, programming and statistics with their applications to economics, finance, medicine, industrial management, natural sciences and others. The programme produces highly qualified students in statistics, operations research and econometrics with applications to economics and business management. The programme provides ideal preparation for a career in economics, health care, finance, banking, insurance, actuarial science, business management, governmental or academic institutions.

In the recent years, mathematical optimization and statistics have experienced significant new developments. With these developments, the system engineering, information science, signal and image processing, statistical error correction and cryptography are being revolutionised.

This has created urgent need, in both academic research and in practical implementation, for a new generation of mathematicians trained to work at the frontiers of mathematical optimization, statistics and their applications to engineering, healthcare, finance and economics.

Researchers at the University of Birmingham have recently shown how the modern optimization and statistical methods are successfully applied to engineering design, financial and economical data analysis, meta-analysis, economic equilibrium, network communication, and combinatorial optimization.

About the School of Mathematics

The School of Mathematics is one of seven schools in the College of Engineering and Physical Sciences. The school is situated in the Watson Building on the main Edgbaston campus of the University of Birmingham. There are about 50 academic staff, 15 research staff, 10 support staff, 60 postgraduate students and 600 undergraduate students.
At the School of Mathematics we take the personal development and careers planning of our students very seriously. Jointly with the University of Birmingham's Careers Network we have developed a structured programme to support maths students with their career planning from when they arrive to when they graduate and beyond.

Funding and Scholarships

There are many ways to finance your postgraduate study at the University of Birmingham. To see what funding and scholarships are available, please visit: http://www.birmingham.ac.uk/postgraduate/funding

Open Days

Explore postgraduate study at Birmingham at our on-campus open days.
Register to attend at: http://www.birmingham.ac.uk/postgraduate/visit

Virtual Open Days

If you can’t make it to one of our on-campus open days, our virtual open days run regularly throughout the year. For more information, please visit: http://www.pg.bham.ac.uk

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This programme will help you develop professionally in the theory and practice of statistics and operational research (OR), providing the foundations for a successful career. Read more

This programme will help you develop professionally in the theory and practice of statistics and operational research (OR), providing the foundations for a successful career.

This programme will prepare you for work in areas such as the medical and health industry, government, the financial sector and any other area where modern statistical tools and OR techniques are used. You will also develop the wider skills required for solving problems, working in teams and time management.

You will be able to identify appropriate statistical or operational techniques, which can be applied to practical problems, and will acquire extensive skills in modelling using the packages R for Statistics and Arena for simulation.

Programme structure

This MSc consists of lecture-based courses and practical, lab-based courses. You will be assessed by exams, written reports, programming assignments and a dissertation project. The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.

Previous compulsory courses for 2017-18:

  • Bayesian Theory
  • Fundamentals of Operational Research
  • Fundamentals of Optimization
  • Generalised Regression Models
  • Methodology, Modelling and Consulting Skills
  • Simulation
  • Statistical Programming
  • Statistical Research Skills

Previous optional courses for 2017-18 include:

  • The Analysis of Survival Data
  • Bayesian Data Analysis
  • Biomedical Data Science
  • Credit Scoring
  • Genetic Epidemiology
  • Incomplete Data Analysis
  • Integer and Combinatorial Optimization
  • Large Scale Optimization for Data Science
  • Machine Learning Practical
  • Nonparametric Regression Models
  • Operational Research in the Energy Industry
  • Python Programming
  • Risk and Logistics
  • Scientific Computing
  • Statistical Consultancy
  • Stochastic Modelling
  • Time Series
  • Topics in Applied Operational Research
  • Topics in Applied Optimization

Career opportunities

This programme is ideal for students who wish to apply their statistics and operational research knowledge within a wide range of sectors including the medical and health sector, government and finance. The advanced problem-solving skills you will develop will be highly prized by many employers.

Industry-based dissertation projects

The dissertation projects of approximately half the students on this programme take place in public and private sector organisations. Other students choose a University-based project.



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This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights and routing mobile phone calls to managing investments and minimising risks. Read more

This programme will show you how to use mathematical techniques to tackle real-life problems ranging from scheduling flights and routing mobile phone calls to managing investments and minimising risks. Operational Research (OR) is an important skill that is in high demand.

Our intensive programme allows you to specialise in an area that best suits your career goals. In addition to this general MSc in Operational Research, the following degrees are offered:

Programme structure

This programme involves two taught semesters of compulsory and option courses followed by your dissertation project.

Courses previously offered include:

Compulsory courses:

  • Fundamentals of Optimization
  • Fundamentals of Operational Research
  • Methodology, Modelling and Consulting Skills
  • Simulation
  • Stochastic Modelling

Plus two courses from:

  • Statistical Methodology
  • Probability and Statistics
  • Object-Oriented Programming with Applications
  • Statistical Programming
  • Scientific Computing
  • Python Programming

Optional courses are generally grouped into the following areas:

  • Finance
  • Industry
  • Optimization
  • Statistics

Career opportunities

The skills you will learn are in demand by a vast range of high-profile organisations including consultancy firms, companies with operational research departments such as airlines or telecommunications providers, financial firms and the public sector.

Recent graduates have joined British Airways, the Government OR Service, Barclays, Deloitte, Capgemini and smaller specialised OR, finance and energy companies.

Industry-based dissertation projects

The dissertation projects of approximately half the students on this programme take place in public and private sector organisations. Other students choose a University-based project.



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The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods. Read more

The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.

Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.

This MSc programme delivers:

  • a flexible programme of study relevant to the needs of employers such as: top investment banks, hedge funds and asset management firms
  • a solid knowledge in financial derivative pricing, risk management and portfolio management
  • the transferable computational skills required by the modern quantitative finance world

Programme structure

You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.

There are two streams: the Financial stream and the Computational stream.

Compulsory courses previously offered include (both streams):

  • Stochastic Analysis in Finance (20 credits, semester 1)
  • Discrete-Time Finance (10 credits, semester 1)
  • Finance, Risk and Uncertainty (10 credits, semester 1)
  • Object-Oriented Programming with Applications (10 credits, semester 1)
  • Risk-Neutral Asset Pricing (10 credits, semester 2)
  • Stochastic Control and Dynamic Asset allocation (10 credits, semester 2)
  • Monte Carlo Methods (5 credits, semester 2)
  • Numerical Methods for Stochastic Differential Equations (5 credits, semester 2)
  • Research-Linked Topics (10 credits, semesters 1 and 2)

Additional compulsory courses for Computational Stream previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)

Additional compulsory courses for Financial stream previously offered include:

  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)

Optional courses previously offered include:

  • Numerical Partial Differential Equations (10 credits, semester 2)
  • Time Series (10 credits, semester 2)
  • Financial Risk Theory (10 credits, semester 2)
  • Optimization Methods in Finance (10 credits, semester 2)
  • Integer and Combinatorial Optimization (10 credits, semester 2)
  • Bayesian Theory (10 credits, semester 1)
  • Credit Scoring (10 credits, semester 2)
  • Python Programming (10 credits, semester 1)
  • Scientific Computing (10 credits, semester 1)
  • Programming Skills - HPC MSc (10 credits, semester 1)
  • Parallel Numerical Algorithms - HPC MSc (10 credits, semester 1)
  • Applied Databases (10 credits)

Work placements/internships

We work closely with the Scottish Financial Risk Academy (SFRA) to offer a number of short courses led by industry (part of our Research-Linked Topics) and to provide the opportunity to our best students to write their dissertations during placements with financial services companies.

Learning outcomes

At the end of this programme you will have:

  • developed personal communications skills, initiative, and professionalism within a mathematical context
  • developed transferable skills that maximise your prospects for future employment, including writing, oral presentation, team-working, numerical and logical problem-solving, planning and time-management
  • improved your ability to convey ideas in an articulate fashion, to build upon previous mathematical training and further develop logic and deductive skills
  • mastered standard and advanced mathematical tools used to solve applied problems relevant to the mathematical finance industry
  • developed quantitative and computational skills for the proficient fulfilment of tasks in the financial sector

Career opportunities

Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.



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Specialize in optimizing company decisions by using data and mathematical models and algorithms. Read more

Course details

Specialize in optimizing company decisions by using data and mathematical models and algorithms
Do you like working with data and mathematics? Do you want to specialize in solving the most complex decision problems, from supply chain management to vehicle routing and from donor kidney allocation to train scheduling? Then Business Analytics and Operations Research is the right program for you.

Apply the theory in practice, and learn to communicate efficiently with decision makers
This program teaches generic Data Science and Optimization methods that allow you to solve decision problems in a wide variety of applications. Examples from logistics include vehicle routing, supply chain optimization, and inventory management. But there are many other applications, such as donor kidney allocation and optimizing tumor treatment plans in the medical field, or dike height optimization and train scheduling in the public sector.

Because Business Analytics and Operations Research are predominantly applied fields, you will also develop the skills to successfully apply the theory in practice, and to communicate efficiently with the decision makers.

The program offers you:

- a degree qualification held in high regard by international organizations that increasingly depend on quantitative methods to support their operations, logistics and supply chain management decision-making.

- a program that is comparable to top Business Analytics and Operations Research programs in the world, both in terms of the contents of the courses and the quality of the teachers and supervisors.

- excellent career perspectives: The Master's program in Business Analytics and Operations Research is one of the programs with the best career prospects. Business Analytics and Operations Research professionals are currently in very high demand, and the demand is predicted to increase even further.

- relevant, real-world learning where the emphasis is on solving actual business problems and turning data into managerial insights, whether it is during in-class exercises or while on an internship in a company.

- BAOR Mastermind, your ideal opportunity to explore the labor market and meet your future employer! We invite companies that give you insight in real business cases. You will also get the opportunity to discuss new trends in Business Analytics and Operations Research with academics and practitioners.

- lectures by teachers and supervisors that are leading experts in this field, with strong links with practice.

- small class-sizes giving you more quality time with approachable and supportive professors and closer interaction with your classmates

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Data science is the study of the computational principles and systems for extracting knowledge from data, for maintaining data, and for ensuring its quality. Read more

Data science is the study of the computational principles and systems for extracting knowledge from data, for maintaining data, and for ensuring its quality. Large data sets are now generated by almost every activity in science, society and commerce.

This EPSRC-sponsored programme tackles the question: how can we efficiently find patterns in these vast streams of data?

Many research areas in informatics are converging on the problem of data science. Those represented in the School include machine learning, artificial intelligence, databases, data management, optimization and cluster computing; and also the unstructured data issues generated in areas such as natural language processing and computer vision.

Our programme will allow you to specialise and perform advanced research in one of these areas, while gaining breadth and practical experience throughout data science.

A short sample of our research interests includes:

  • machine learning applied to problems in biology, astronomy, computer science, engineering, health care, and e-commerce
  • database theory and technology for managing unstructured data and for maintaining trust in data
  • big data and management of streaming data
  • management of unstructured data, including natural language processing, speech processing, and computer vision

Many more topics can be found by exploring the Centre’s web pages, particularly the personal web pages of the Centre supervisors:

You will be supervised by one of our 58 world-renowned faculty. You will also benefit from interacting with a group of 35 leading industrial partners, including Amazon, Apple, Google, IBM, and Microsoft.

This will ensure your research is informed by real world case studies and will provide a source of diverse internship opportunities. Moreover we believe that key research insights can be gained by working across the boundaries of conventional groupings.

Training and support

The MScR is the first part of a longer 1+3 (MSc by Research + PhD) programme offered by the School through the EPSRC.

Our four-year PhD programme combines masters level coursework and project work with independent PhD-level research.

In the first year, you will undertake six masters level courses, spread throughout machine learning, databases, statistics, optimization, natural language processing, and related areas. You will also undertake a significant introductory research project. (Students with previous masters-level work in these areas may request to take three courses and a larger project, instead of six courses.)

At the end of the first year, successful students will be awarded an MSc by Research. From this basis, the subsequent three years will be spent developing and pursuing a PhD research project, under the close supervision of your primary and secondary supervisors.

You will have opportunities for three to six month internships with leading companies in your area, and to participate in our industrial engagement programme, exchanging ideas and challenges with our sponsor companies.

Throughout your studies, you will participate in our regular programmes of seminars, short talks and brainstorming sessions, and benefit from our pastoral mentoring schemes.

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

Facilities

Our research groups contain a diverse range of compute clusters for compute and data-intensive work, including a large cluster hosted by the Edinburgh Compute and Data Facility.

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

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

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

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

Career opportunities

We intend for our graduates to become the research leaders, both in industry and academia, whose work will lead the way in data science. This vision is shared by our industrial supporters, whose support for our internship programme indicates their strong desire to find highly qualified new employees.

You will be part of a new generation of data scientists, with the technical skills and interdisciplinary awareness to become R&D leaders in this emerging area.

Our component research groups already have excellent track-records in post-graduation destinations, including the research labs of industry-leading companies, and post-doctoral research positions in top tier universities.



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In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. Read more

In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.

This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:

  • classical and Bayesian ideologies
  • computational statistics
  • regression
  • data analysis of a range of models and applications

Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.

Programme structure

To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits which will be a combination of compulsory and optional courses. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The standard dissertation will take the form of two consultancy-style case projects in different application areas.

The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.

Previous compulsory courses for 2017-18:

  • Bayesian Data Analysis
  • Bayesian Theory
  • Generalised Regression Models
  • Incomplete Data Analysis
  • Statistical Programming
  • Statistical Research Skills

Previous optional courses for 2017-18 include:

  • The Analysis of Survival Data
  • Biomedical Data Science
  • Credit Scoring
  • Fundamentals of Operational Research
  • Fundamentals of Optimization
  • Genetic Epidemiology
  • Large Scale Optimization for Data Science
  • Machine Learning and Pattern Recognition
  • Machine Learning Practical
  • Nonparametric Regression Models
  • Object-Oriented Programming with Applications
  • Probabilistic Modelling and Reasoning
  • Python Programming
  • Scientific Computing
  • Statistical Consultancy
  • Statistical Methodology
  • Stochastic Modelling
  • Time Series

Learning outcomes

At the end of this programme you will have:

  • knowledge and understanding of statistical theory and its applications within data science
  • the ability to formulate suitable statistical models for new problems, fit these models to real data and correctly interpret the results
  • the ability to assess the validity of statistical models and their associated limitations
  • practical experience of implementing a range of computational techniques using statistical software R and BUGS/JAGS

Career opportunities

Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.

Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.

The degree is also excellent preparation for further study in statistics or data science.



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This Masters programme trains graduates of engineering, science or related disciplines in general and specialist process systems engineering subjects. Read more

This Masters programme trains graduates of engineering, science or related disciplines in general and specialist process systems engineering subjects.

Such areas are not generally covered in engineering and science curricula, and BSc graduates tend to be ill prepared for the systems challenges they will face in industry or academia on graduation.

Programme structure

This programme is studied full-time over one academic year and part-time students must study at least two taught technical modules per academic year. It consists of eight taught modules and a dissertation.

Example module listing

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.

Facilities, equipment and academic support

Modules related to the different groups are taught by a total of six full-time members of staff and a number of visiting lecturers.

As part of their learning experience, students have at their disposal a wide range of relevant software needed to support the programme material dissertation projects. In recent years, this work included the design of various knowledge-based and business systems on the internet, the application of optimisation algorithms, and semantic web applications.

Numerous laboratory facilities across the Faculty and the University are also available for those opting for technology-based projects, such as the process engineering facility, a control and robotics facility and signal processing labs.

The work related to the MSc dissertation can often be carried out in parallel with, and in support of, ongoing research. In the past, several graduates have carried on their MSc research to a PhD programme.

Career prospects

Engineers and scientists are increasingly expected to have skills in information systems engineering and decision-support systems alongside their main technical and/or scientific expertise.

Graduates of this programme will be well prepared to help technology-intensive organisations make important decisions in view of vast amounts of information by adopting, combining, implementing and executing the right technologies.

Educational aims of the programme

The programme aims to provide a highly vocational education which is intellectually rigorous and up-to-date. It also aims to provide the students with the necessary skills required for a successful career in the process industries.

This is achieved through a balanced curriculum with a core of process systems engineering modules supplemented by a flexible element by way of elective modules that permit students to pursue an element of specialisation relevant to their backgrounds, interests and/or career aspirations.

An integrated approach is taken so as to provide a coherent view that explores the interrelationships between the various components of the programme. The programme draws on the stimulus of the Faculty’s research activities.

The programme provides the students with the basis for developing their own approach to learning and personal development.

Programme learning outcomes

Knowledge and understanding

  • State-of- the-art knowledge in process systems engineering methods, in the areas of: modelling and simulation of process systems, mathematical optimization and decision making, process systems design, supply chain management, process and energy integration, and advanced process control technologies
  • Advanced level of understanding in technical topics of preference, in one or more of the following aspects: renewable energy technologies, refinery and petrochemical processes, biomass processing technologies, and knowledge-based systems

Intellectual / cognitive skills

The programme aims to strengthen cognitive skills of the students, particularly in the aspects of problem definition, knowledge and information acquiring, synthesis, and creativity, as collectively demonstrable through the successful completion of the research dissertation. The key learning outcomes include the abilities to:

  • Select, define and focus upon an issue at an appropriate level
  • Collect and digest knowledge and information selectively and independently to support a particular scientific or engineering enquiry
  • Develop and apply relevant and sound methodologies for analysing the issue, developing solutions, recommendations and logical conclusions, and for evaluating the results of own or other’s work

Professional practical skills

The programme primarily aims to develop skills for applying appropriate methods to analyse, develop, and assess process systems and technologies. The key learning outcomes include the abilities to:

  • Assess the available systems in the process industries
  • Design and/or select appropriate system components, and optimise and evaluate system design
  • Apply generic systems engineering methods such as modelling, simulation, and optimization to facilitate the assessment and development of advanced process technologies and systems

Key / transferable skills

The programme aims to strengthen a range of transferable skills which are relevant to the needs of existing and future professionals in knowledge intensive industries irrespective of their sector of operation. The key learning outcomes include the further development of the skills in the following areas:

  • Preparation and delivery of communication and presentation
  • Report and essay writing
  • Use of general and professional computing tools
  • Collaborative working with team members
  • Organising and planning of work
  • Research into new areas, particularly in the aspect of literature review and skills acquisition

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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This programme explores technology across a wide scope of engineering disciplines and will train you in general and specialist process systems engineering – crucial aspects for finance, industrial management and computer-integrated manufacturing. Read more

This programme explores technology across a wide scope of engineering disciplines and will train you in general and specialist process systems engineering – crucial aspects for finance, industrial management and computer-integrated manufacturing.

There is a wide selection of modules on offer within the programme. All taught modules are delivered by qualified experts in the topics and academic members of University staff, assisted by specialist external lecturers.

Our programme combines high-quality education with substantial intellectual challenges, making you aware of current technologies and trends while providing a rigorous training in the fundamentals of the subject.

Programme structure

This programme is studied full-time over one academic year and part-time students must study at least two taught technical modules per academic year. It consists of eight taught modules and a dissertation.

Example module listing

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.

Educational aims of the programme

The programme combines advanced material in two popular and complementary topics: systems engineering and environmental engineering. The key learning outcome is a balanced combination of systems and environmental skills and prepares students in a competitive market where both topics appear attractive.

The programme will provide training in general and specialist process and environmental systems engineering subjects, and prepare the students for the systems challenges they will face in industry or academia upon graduation.

The programme disseminates technology with a wide scope among engineering disciplines, with a wide selection of modules on offer. All taught modules are delivered by qualified experts in the topics and academic members of the university staff, assisted by specialist external lecturers.

The programme provides high-quality education with substantial intellectual challenges, commensurate with the financial rewards and job satisfaction when venturing into the real world. A key component is to make the student aware of current technologies and trends, whilst providing a rigorous training in the fundamentals of the subject.

Programme learning outcomes

Knowledge and understanding

The programme aims to develop the knowledge and understanding in both process and environmental systems engineering. The key learning outcomes include:

  • State-of- the-art knowledge in process and environmental technologies, in the areas of: life cycle assessment and sustainable development, modelling and simulation of process systems, mathematical optimization and decision making, process systems design, and process and energy integration
  • Advanced level of understanding in technical topics of preference, in one or more of the following aspects: general renewable energy technologies, and solar energy in particular; advanced process control

Intellectual / cognitive skills

The programme aims to strengthen cognitive skills of the students, particularly in the aspects of problem definition, knowledge and information acquiring, synthesis, and creativity, as collectively demonstrable through the successful completion of the research dissertation.

The key learning outcomes include the abilities to:

  • Select, define and focus upon an issue at an appropriate level
  • Collect and digest knowledge and information selectively and independently to support a particular scientific or engineering enquiry
  • Develop and apply relevant and sound methodologies for analysing the issue, developing solutions, recommendations and logical conclusions, and for evaluating the results of own or other’s work

Professional practical skills

The programme primarily aims to develop skills for applying appropriate methods to analyse, develop, and assess process and environmental systems and technologies. The key learning outcomes include the abilities to:

  • Assess the available systems in the process industries with focus on environmental challenges
  • Design and/or select appropriate system components, and optimise and evaluate system design
  • Apply generic systems engineering methods such as modelling, simulation, and optimization to facilitate the assessment and development of advanced process and environmental technologies and systems

Key / transferable skills

The programme aims to strengthen a range of transferable skills which are relevant to the needs of existing and future professionals in knowledge intensive industries irrespective of their sector of operation. The key learning outcomes include the further development of the skills in the following areas:

  • Preparation and delivery of communication and presentation
  • Report and essay writing
  • Use of general and professional computing tools
  • Collaborative working with team members
  • Organising and planning of work
  • Research into new areas, particularly in the aspect of literature review and skills acquisition

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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This programme will equip you with the essential knowledge for engineering careers in the oil, gas and petrochemical sectors. Read more

This programme will equip you with the essential knowledge for engineering careers in the oil, gas and petrochemical sectors.

Upon completion of the course you will have gained a comprehensive understanding of oil refining and associated downstream processing technologies, operations and economics; process safety and operations integrity; and methods for the optimal design of process systems.

You will learn about the general economics of the energy sector, oil exploration and production, as well as renewable energy systems.

Furthermore, your study of the various aspects of petroleum refining will be augmented by unique work assignments at a virtual oil-refining and chemical company.

Programme structure

This programme is studied full-time over one academic year and part-time students must study at least two taught technical modules per academic year. It consists of eight taught modules and a dissertation.

Example module listing

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.

Educational aims of the programme

The programme aims to provide a highly vocational education that equips the students with the essential knowledge and skills required to work as competent engineers in the petrochemical sector.

This is to be achieved through combining proper material in two popular and complementary topics: process systems engineering and petroleum refining. The key objective is to develop a sound understanding of oil refining and downstream processing technologies, process safety and operation integrity, as well as systems methods for the optimal design of process systems.

A balanced curriculum is provided with essential modules from these two areas supplemented by a flexible element by way of elective modules that permit students to pursue subjects of preference relevant to their backgrounds, interests and/or career aspirations.

An integrated approach is taken so as to provide a coherent view that explores the interrelationships between the various components of the programme.

Programme learning outcomes

Knowledge and understanding

The programme aims to develop the knowledge and understanding in both petroleum refining and systems engineering. The key learning outcomes include:

  • State-of- the-art knowledge in petroleum refining and petrochemical processing, in terms of the technologies of processes that comprise a modern refinery and petrochemicals complex
  • The principles for analysing and improving the profitability of refining and petrochemicals processing
  • General Safety, health, and environment (SHE) principles on a refinery and petrochemicals complex
  • Methods and systems for ensuring safe and reliable design and operation of process units
  • State-of- the-art knowledge in process systems engineering methods, in the areas of: modelling and simulation of process systems, mathematical optimization and decision making, process systems design and process and energy integration
  • Advanced level of understanding in technical topics of preference, in one or more of the following aspects: petroleum exploration and production, economics of the energy sector, sustainable and renewable systems, supply chain management

Intellectual / cognitive skills

The programme aims to strengthen cognitive skills of the students, particularly in the aspects of problem definition, knowledge and information acquiring, synthesis, and creativity, as collectively demonstrable through the successful completion of the research dissertation.

The key learning outcomes include the abilities to:

  • Select, define and focus upon an issue at an appropriate level
  • Collect and digest knowledge and information selectively and independently to support a particular scientific or engineering enquiry
  • Develop and apply relevant and sound methodologies for analysing the issue, developing solutions, recommendations and logical conclusions, and for evaluating the results of own or other’s work

Professional practical skills

The programme primarily aims to develop skills for applying appropriate methods to the design and operation of petroleum refining processes. The key learning outcomes include the abilities to:

  • Apply knowledge of the operation of refineries to analyze and to improve the profitability of refining and petrochemical processing
  • Apply relevant principles, methods, and tools to improve the safety and operation integrity of refineries
  • Apply systems engineering methods such as modelling, simulation, optimization, and energy integration to improve the design of petroleum refining units and systems

Key / transferable skills

The programme aims to strengthen a range of transferable skills that are relevant to the needs of existing and future professionals in knowledge intensive industries irrespective of their sector of operation.

The key learning outcomes include the further development of the skills in the following areas:

  • Preparation and delivery of communication and presentation
  • Report and essay writing
  • Use of general and professional computing tools
  • Collaborative working with team members
  • Organizing and planning of work
  • Research into new areas, particularly in the aspect of literature review and skills acquisition

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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Created in the context of the rapid advancement of the renewable-energy industry, this Masters programme investigates both renewable energy and systems technologies. Read more

Created in the context of the rapid advancement of the renewable-energy industry, this Masters programme investigates both renewable energy and systems technologies.

It is designed to build your competence and confidence in the R&D and engineering tasks that are demanded of scientific engineers in the renewable and sustainable-development sector.

Programme structure

This programme is studied full-time over one academic year and part-time students must study at least two taught technical modules per academic year. It consists of eight taught modules and a dissertation.

Example module listing

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.

Facilities, equipment and academic support

Modules related to the different groups are taught by a total of six full-time members of staff and a number of visiting lecturers.

As part of their learning experience, students have at their disposal a wide range of relevant software needed to support the programme material dissertation projects. In recent years, this work included the design of various knowledge-based and business systems on the internet, the application of optimisation algorithms, and semantic web applications.

Numerous laboratory facilities across the Faculty and the University are also available for those opting for technology-based projects, such as the process engineering facility, a control and robotics facility and signal processing labs.

The work related to the MSc dissertation can often be carried out in parallel with, and in support of, ongoing research. In the past, several graduates have carried on their MSc research to a PhD programme.

Career prospects

Engineers and scientists are increasingly expected to have skills in information systems engineering and decision-support systems alongside their main technical and/or scientific expertise.

Graduates of this programme will be well prepared to help technology-intensive organisations make important decisions in view of vast amounts of information by adopting, combining, implementing and executing the right technologies.

Educational aims of the programme

This programme investigates both renewable energy and systems technologies in order to produce scientific researchers and engineers who are competent in the R&D and engineering tasks applicable to the renewable energy and sustainable development sectors.

Its primary aims lie in developing a global understanding of the major types of renewable energy technologies, in-depth knowledge of the technology for biomass-based renewable energy, and knowledge and skills in systems modelling and optimisation.

A balanced curriculum will be provided with a core of renewable energy and systems engineering modules supplemented by a flexible element by way of elective modules that permit students to pursue an element of specialisation relevant to their backgrounds, interests and/or career aspirations.

An integrated approach is taken so as to provide a coherent view that explores the interrelationships between the various components of the programme.

Programme learning outcomes

Knowledge and understanding

The programme aims to develop the knowledge and understanding in both renewable energy and systems engineering. The key learning outcomes include:

  • State-of- the-art knowledge in renewable energy technologies, in terms of: the sources, technologies, systems, performance, and applications of all the major types of renewable energy; approaches to the assessment of renewable energy technologies; the processes, equipment, products, and integration opportunities of biomass-based manufacturing
  • State-of- the-art knowledge in process systems engineering methods, in the areas of: modelling and simulation of process systems; mathematical optimization and decision making; process systems design
  • Advanced level of understanding in technical topics of preference, in one or more of the following aspects: process and energy integration, economics of the energy sector, sustainable development, supply chain management

Intellectual / cognitive skills

The programme aims to strengthen cognitive skills of the students, particularly in the aspects of problem definition, knowledge and information acquiring, synthesis, and creativity, as collectively demonstrable through the successful completion of the research dissertation. The key learning outcomes include the abilities to:

  • Select, define and focus upon an issue at an appropriate level
  • Collect and digest knowledge and information selectively and independently to support a particular scientific or engineering enquiry
  • Develop and apply relevant and sound methodologies for analysing the issue, developing solutions, recommendations and logical conclusions, and for evaluating the results of own or other’s work

Professional practical skills

The programme primarily aims to develop skills for applying appropriate methods to analyze, develop, and assess renewable technologies and systems. The key learning outcomes include the abilities to:

  • Assess the available renewable energy systems
  • Design and select appropriate collection and storage, and optimise and evaluate system design
  • Apply generic systems engineering methods such as modelling, simulation, and optimization to facilitate the assessment and development of renewable energy technologies and systems

Key / transferable skills

The programme aims to strengthen a range of transferable skills which are relevant to the needs of existing and future professionals in knowledge intensive industries irrespective of their sector of operation. The key learning outcomes include the further development of the skills in the following areas:

  • Preparation and delivery of communication and presentation
  • Report and essay writing
  • Use of general and professional computing tools
  • Collaborative working with team members
  • Organizing and planning of work
  • Research into new areas, particularly in the aspect of literature review and skills acquisition

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.



Read less
What is the Master of Food Technology all about?.  The Interuniversity Programme in Food Technology (IUPFOOD) focuses on . Read more

What is the Master of Food Technology all about?

 The Interuniversity Programme in Food Technology (IUPFOOD) focuses on two technological dimensions of prime and crucial importance in food processing and preservation:

  • the transformation (processing) of raw materials into products suited for human consumption
  • the role of postharvest and food preservation unit operations in delivering safe and nutritious foods to the end consumer.

These two concerns are directly translated in the focus points of the IUPFOOD programme.

The InterUniversity Programme in Food Technology (IUPFOOD) is jointly organised by KU Leuven and Ghent University (UGent). The programme builds on KU Leuven’s and UGent’s combined expertise in research and education in the field of food technology.

Structure

The Master of Science in Food Technology (120 ECTS) consists of four major segments:

  • In-depth education segment (60 ECTS)
  • Specialisation segment (18 ECTS)
  • Elective courses segment (12 ECTS)
  • Master’s thesis segment (30 ECTS) 

 In the first year of the Master's programme, students will spend the first semester in Ghent and the second semester in Leuven. The second stage courses of the majors 'Postharvest and Food Preservation Engineering' and 'Food Science and Technology' are taught respectively at KU Leuven and UGent; at both universities, optional courses and thesis research topics are offered.

Objectives

1. Has profound and detailed scientific knowledge and understanding of the (bio)chemical processes in biological raw materials during postharvest storage and their transformation into food products.

2. Has profound and detailed scientific knowledge and understanding of engineering principles of unit operations and their use in the transformation of raw materials into food products as a basis for qualitative and quantitative design, evaluation and optimization of food process and preservation unit operations.

3. Has profound and detailed scientific knowledge and understanding of ecology, physiology, detection, use and combat microorganisms in food systems.

4. Has profound and detailed scientific knowledge and understanding of (bio)-chemical, physical and microbiological methods for analysis of raw materials and foods including the skills to identify and use such methods in the context of research, process and product design and optimization and food control.

5. Has profound and detailed scientific knowledge in different fields of product technology such as vegetable products, dairy products, meat products, fish products, cereal derived products and fermented products including aspects of product development in relation to consumer behavior.

6. Can critically evaluate the functionality and safety of foods in the context of human health including the relation with raw materials and their processing into foods based on analytical data and scientific literature data.

7. Masters the skills and has acquired the problem solving capacity to analyze problems of food quality and safety along the food chain and to elaborate interdisciplinary and integrated qualitative and quantitative approaches and solutions (including implementation) appreciating the complexity of food systems and the processes used while taking into account technical limitations and socio-economic aspects such as feasibility, risks, and sustainability.

8. Has acquired a broad perspective to problems of food security, related to postharvest and food processing, in low income developing countries.

9. Can investigate and understand interaction with other relevant science domains and integrate them within the context of more advanced ideas and practical applications and problem solving.

10. Can demonstrate critical consideration of and reflection on known and new theories, models or interpretation within the broad field of food technology.

11. Can identify and apply appropriate research methods and techniques to design, plan and execute targeted experiments or simulations independently and critically evaluate and interpret the collected data.

12. Can develop and execute independently original scientific research and/or apply innovative ideas within research environments to create new and/or improved insights and/or solutions for complex (multi)disciplinary research questions respecting the results of other researchers.

13. Can convincingly and professionally communicate personal research, thoughts, ideas, and opinions of proposals, both written and oral, to different actors and stakeholders from peers to a general public.

14. Has acquired project management skills to act independently and in a multidisciplinary team as team member or team leader in international and intercultural settings.

Career perspectives

IUPFOOD's objective is to offer a programme that takes the specific needs and approaches of developing countries into account. The IUPFOOD programme prepares graduates for various tasks, including teaching and research. IUPFOOD alumni are mainly active in the following sectors:

  • academic institutions (as teaching and/or research staff)
  • research institutes (as research staff)
  • nongovernmental organisations (in different capacities)
  • governmental institutes (e.g. in research programmes, quality surveillance programmes or national nutritional programmes)
  • private industry (in particular jobs related to quality control)


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