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Predictive Modelling and Scientific Computing MSc/PGDip/PGCert/PGA

Postgraduate Taught

Course code

P-H1B1 (MSc)

P-H1B5 (PGDip)

P-H1B6 (PGCert)

P-H1B7 (PGA)

Start date

27 September 2027

Study location

天美传媒

Qualification

Postgraduate Award (PGA)

Postgraduate Certificate (PGCert)

Postgraduate Diploma (PGDip)

Master of Science (MSc)

Department

School of Engineering

Duration

Full-time: 1 year (MSc), 9 months (PGDip, PGCert, PGA)

Part-time: 2 years (MSc), 18 months (PGDip, PGCert), 1 year (PGA)

Course overview

Modelling solutions to our biggest challenges.

From climate change to healthcare, today鈥檚 global challenges require new ways to predict complex systems. Our MSc gives you the computational science and engineering skills to develop practical solutions. Building on world-leading research at the Warwick Centre for Predictive Modelling, you鈥檒l explore the potential of advanced modelling techniques, machine learning and scientific computing. With strong links to industry, you鈥檒l graduate with in-demand skills for a wide range of sectors, from research and development to policy-making.

School of Engineering students

I鈥檝e been able to successfully apply a plethora of skills that I have gained from the course, both computational and mathematical, at my place of work. It has transformed me into a better data scientist, programmer, and computational scientist.

Karan

MSc Predictive Modelling and Scientific Computing graduate

Modules

Learn to apply cutting-edge thinking to global challenges.

  • Advanced modelling: develop expertise in mathematical modelling, numerical algorithms, optimisation, statistics, machine learning and data science, including the techniques that underpin advances in AI
  • Real-world applications:鈥痑pply computational techniques and engineering to challenges across areas including environmental science, energy, healthcare, materials engineering, food science and geology
  • Research-led learning:鈥痓uild on cutting-edge research from the Warwick Centre for Predictive Modelling and explore how modelling can improve technology and design
  • Interdisciplinary teaching: part of Warwick鈥檚 flagship Science for Sustainable Futures portfolio, this course brings together expertise from Engineering, Mathematics, WMG and other leading departments and research centres. Students learn across traditional subject boundaries while developing the critical thinking and practical skills valued by employers across a wide range of industries.
  • Independent project:鈥痗omplete a substantial research project, bringing new knowledge, methodology or understanding to a topic aligned with your interests

Note that information found by following the links to the module catalogue is subject to change for future years of study, as we evolve our courses in response to the latest developments in academia and industry.

Optional modules can vary from year to year. Example optional modules may include:

Please note that this list is not exhaustive and other optional modules are available.

Teaching and learning

Learn from specialists at the forefront of predictive modelling and computational research.

  • Computational experience:鈥痷se advanced tools and techniques to understand complex systems
  • Industry-focused skills:鈥�develop practical abilities valued across sectors including industrial R&D, technology, pharmaceuticals, materials and energy - we have strong links with a range of potential employers
  • Flexible study options:鈥痗hoose from MSc, PG Diploma, Certificate and Award routes, with part-time study across two years to fit around your commitments

Entry requirements

Fees and scholarships

Tuition fees are payable for each year of your course at the start of the academic year, or at the start of your course, if later. Academic fees cover the cost of tuition, examinations and assessment and some student amenities.

Find your course fees

About the department

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Your university experience is defined by far more than your course or the career path you follow. At Warwick, it鈥檚 where you discover who you could become.

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