MSc student projects with industry partners

The MSc project is a vital component of all MSc programmes here at Manchester. Each year we have a number of projects that are sourced from industry partners who work in partnership with academic supervisors to support MSc students in the completion of their project. Such partnerships represent a unique opportunity for companies to raise their profile with the next generation of computer scientists whilst securing resource for business critical projects.

If you are interested in providing MSc project proposals we would be delighted to discuss things with you in further detail.

MSc Student Projects outlines some general information about our process and expectations.

Whilst projects are completed to the timescales set our below, we are keen to hear from companies at all times of year.

Please direct initial enquiries to

MSc Project Timeline


  • Submission of project ideas by late August/early September 

Semester 1

  • Final specification of project agreed by end of October
  • Selection of projects by students in December

Semester 2

  • Notification of project allocation to students in January
  • Initial contact between student, supervisor and company in February
  • Background research from February to May 


  • Full time project work from May to August
  • Final submission date in early September

Student Projects: Astrazeneca MSc Project Profile

MSc Project Spotlight - AstraZeneca

As a result of a partnership with Astrazeneca, Computer Science MSc students working under the supervision of Dr Gavin Brown, Professor Andy Brass and Professor Uli Sattler, have been given the opportunity to take on some interesting and challenging projects in the field of BioHealth Informatics.

The projects are sponsored by Dr James Weatherall, Astrazeneca's Global Director for Biomedical Informatics, and will see students working in close collaboration with staff at the Alderley Edge research site.

One such project, being undertaken by MSc student Elisabeta Marinoiu, will apply feature selection to the problem of adverse event selection. Feature selection is a statistical technique used in machine learning to automatically identify which features (biomarkers) within a given set of data are relevant, irrelevant or redundant in the context of other features. These techniques can be employed to help practitioners identify critical features and understand the interdependencies between them.

Elisabeta's project is informed by the challenge of personalised medicine. During the pharmaceutical development of a drug it is vital that all safety signals are investigated and considered. Within a single clinical trial, patients are treated at different times and at different doses of a drug and adverse events are reported over the course of the trial.

Elisabeta's project will analyse time-dependent data from a clinical trial in order to select the key features of a patient (age, sex, BMI etc.) that predict whether a patient experiences a particular adverse effect, and when. The project will focus on a framework of mutual information and will deliver a prototype implementation of the solution.

Commenting on the projects Dr Brown said:

The partnership with Astrazeneca is providing a unique opportunity for students here in Manchester whilst facilitating knowledge transfer from academic research to industry practice. We're proud to be working with AZ on a topic as pressing and important as cancer prognosis.
▲ Up to the top