Independent Research Project

IRP vivas are 8-12 September 2025

All internal project vivas are open to the public. Please check the schedule and attend the sessions you are interested in.

More information about vivas can be found in Viva Guidelines.

IRP-specific Academic Integrity Rules and Expectations

IRP-specific rules and expectations around academic integrity are in Academic Integrity and Misconduct. All students are responsible for understanding and adhering to all of them. A lack of familiarity with these requirements will not be accepted as a justification in cases of suspected academic misconduct.

Ada Lovelace Academy Launch Event

Join us for the Ada Lovelace Academy launch on Friday, 5 September 2025 (16:00-18:00 BST) at the Main Entrance, South Kensington Campus. This exciting student-centred event offers the opportunity to showcase your work and network with leading industrial partners including BP, IBM, Mars, SSE, and Hitachi.

Apply to present your IRP poster by Friday, 11 July - only 40 spots available! More information: Ada Lovelace Academy Launch Event.

Anonymous Feedback

For anonymous feedback, please use the feedback form, and select IRP for question 1.

Independent Research Project (IRP) is the capstone module in the Ada Lovelace Academy at Imperial College London for the following MSc courses:

• Applied Computational Science and Engineering (ACSE)

• Environmental Data Science and Machine Learning (EDSML)

• Geo-Energy with Machine Learning and Data Science (GEMS)

It allows students to work independently to address a relevant real-world research question or tackle a commercial challenge by designing and implementing a computational solution. It is worth 30 ECTS - one-third of the total 90 ECTS required for the award of the Master’s degree. It takes place over the summer following the completion of the taught modules (for exact IRP dates, see Important dates, and for the curriculum, see MSc curriculum). Upon successful completion of the IRP, students should be able to:

• Develop a computational solution from scratch, substantially extend the capabilities of an existing code, or build a model to analyse and interpret a substantial dataset.

• Contribute to an active research field or a commercial project.

• Creatively and critically employ techniques to solve open-ended, real-world, challenging problems.

• Effectively communicate their work by writing reports and defending it under critical questioning.

Project requirements

The projects can come from different fields. For instance, in previous years, students worked on computational research and commercial projects in environmental science, geoscience and engineering, fundamental and applied physics, mathematics, chemistry, healthcare, finance, computer science, climate modelling, and many others. However, all projects must be computational and include a substantial coding component to address a relevant research question or tackle a commercial challenge. Specifically, a student is expected to either:

• Develop from scratch a simulation code, data processing or analysis pipeline, or machine learning model, to address a non-trivial problem involving a complex system, numerical model, or substantial dataset. OR

• Substantially extend or enhance an existing simulation codebase, data processing or analysis pipeline, or machine learning framework, and apply it to a substantial dataset or computational problem, for example by improving model performance, adding new components, or broadening applicability.

Note

For the ACSE and EDSML MSc courses, the projects can come from any field. On the other hand, for the GEMS MSc course, the project must address a geoscience and engineering problem.

We also suggest referring to the marking criteria section in Marking criteria for an overview of what examiners are expected to evaluate.

Collaboration and teamwork

Multiple students may work on the same project topic, either as part of a coordinated team effort or through parallel but related investigations. In such cases, students can collaborate by working on individual components of a larger project, or by tackling the same problem using different approaches or methodologies. Collaboration between students is encouraged; however, each student submits their own written reports and code, and it must be unambiguous from the submitted work what their contribution is. For further guidance on acceptable collaboration practices and attribution, please see the Academic Integrity and Misconduct.

MSc curriculum

Being updated…

Fig. 1 The IRP is the last module students do after the completion of taught modules.