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Mathematics for Real-World Systems Centre for Doctoral Training

MSc Structure

The MathSys MSc year is structured to provide students with the mathematical training necessary to tackle key challenges facing science, business, and society.

It is dedicated to developing a broad portfolio of mathematical techniques through taught modules covering subjects such as Stochastic Modelling and Random Processes, Numerical Algorithms and Optimisation, Data Analysis and Machine Learning, and Topics in Mathematics Modelling. Alongside this, MSc students also undertake group and individual research projects, working on research problems that have a strong emphasis on applied questions and practical approaches, and that are linked to real-world problems and experiences from the CDT's external collaborative partners.

MathSys is a time-limited CDT and is not recruiting new students.

Term 1

  • MA933 Stochastic Modelling and Random Processes (15 CATS), weeks 1-10 (time spent in lectures/classes: 4 hours per week)
  • MA930 Data Analysis and Machine Learning (15 CATS), weeks 1-5 (time spent in lectures/classes: 8 hours per week)
  • MA934 Numerical Algorithms and Optimisation (15 CATS), weeks 6-10 (time spent in lectures/classes: 8 hours per week)

Term 2

  • MA999 Topics in Mathematical Modelling (15 CATS), weeks 1-10 (time spent in lectures/classes: 4 hours per week)
  • Two taught optional modules equating to 30 CATS, such as those listed hereLink opens in a new window

Other core modules

  • MA932 MSc Research Study Group Project (40 CATS), begins in term 2 and runs through Easter vacation and into the start of term 3
  • MA931 MSc Individual Research Project (50 CATS), runs from mid-June to September

Other compulsory activities

  • Regular MSc cohort meetings (usually weekly) with the MSc Coordinator and CDT Administrator to discuss general topics
  • Intro to Computing workshops (during Welcome Week) and attendance at Computational Techniques classes
  • Attendance at the Complexity/MathSys Forum
  • Transferable Skills activities including compulsory Responsible Research and Ethics training
  • Attendance at CDT Annual Retreat, Summer School, and other events/activities as requested by the CDT and as outlined in the Terms and Conditions and the Student Handbook