Research Assistant in AI Research Education (Fixed Term)
Fixed-term: The funds for this post are available until 30 September 2026.
AI and deep learning are becoming increasingly accessible topics for talented high schoolers, who are now engaging with the topic in a multitude of ways, including new international AI Olympiads (IOAI, IAIO) as well as project-based learning opportunities, such as the Nagymaros AI Retreats (airetreat.org). Our lab is interested in designing high-quality research-focussed curricula and educational material that meets the needs of the highest potential future innovators in the space.
We seek to appoint a research assistant to contribute to developing material targeted at high-school students with the aim of exposing them to exciting state-of-the-art research problems in a way that is suitable for their background knowledge and level. The challenge will be to identify captivating areas of machine learning research which also can serve as great learning problems and build on high-school students interests in algorithms and mathematics. Examples may include interesting application of machine learning to interesting mathematical datasets or problems, reinforcement learning for playing interesting boardgames or puzzles, neural algorithmics, AI for science, etc.
This activity will be embedded in our research lab, with projects spanning areas of the theory of deep learning, robust machine learning, probabilistic deep learning and adjacent areas. This position will contribute to the research programme "Advancing Modern Data-Driven Robust AI", which is funded by UKRI through a Turing AI World-Leading Fellowship led by co-investigators Professor Zoubin Ghahramani (Department of Engineering) and Dr Ferenc Huszár (Department of Computer Science and Technology), and in particular to our around outreach and widening participation.
This Research Assistant will be based at the Department of Computer Science and Technology (affectionately known as the Computer Lab) and will work primarily with Professor Ferenc Huszár (Computer Laboratory) as well as other members of the Machine Learning Groups at the Computer Science and Engineering Departments. Ferenc is the founder of Nagymaros AI Retreats and is a member of the IOAI International Scientific Committee, thus this is a great opportunity to have immediate impact through these organisations.
The core responsibilities include developing educational material in certain formats, with particular focus on well-designed software libraries, Olympiad-style challenge tasks, educational Kaggle-style competitions, syllabuses and potentially training websites, videos, tutorials, etc. Emphasis will be placed on evidence-based development of curricula and an evaluation of the success and downsides of various styles of materials and projects. Additional responsibilities include helping with the coordination of outreach, training and competition activities in the scope of this role. Team and Environment
The broader project spans two departments. This position will be based in the Computer Lab and will be embedded in the ML@CL (Machine Learning at the Computer Lab) group which includes Professor Neil Lawrence, Dr Carl Henrik Ek, Dr Challenger Mishra and Dr Ferenc Huszár as well as several other research fellows and students. The RA will have opportunities to collaborate with the Machine Learning Group at the Engineering Department to work alongside Professors Richard Turner, Carl Edward Rasmussen, Zoubin Ghahramani, and Miguel Hernández-Lobato as well as several research fellows and students. The Computer Science Department also benefits from a partnership with the Raspberry Pi Foundation focussing on computer science education in schools.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Our group values an open and inclusive culture. Members of the research group will be encouraged to engage in activities aimed at widening participation in Machine Learning Research, for example by contributing to summer schools, mentoring applicants and students from a variety of backgrounds.
Expected Qualifications
A strong degree in computer science/mathematics/engineering or equivalent experience
Strong programming experience in Python and knowledge of machine learning libraries/frameworks such as NumPy, PyTorch, JAX or Tensorflow.
Prior experience in completing relevant machine learning projects or coursework during formal education.
Past relevant experience as a software engineer or data scientist in an industry context, and demonstrably good software engineering practices and experience producing high-quality, reproducible code including unit tests, documentation will be considered an advantage.
Beneficial Qualifications
Past experience with computing and mathematics olympiads, gifted education, either as a student, or as a mentor/teacher.
Past relevant experience as a software engineer or data scientist in an industry context, and demonstrably good software engineering practices and experience producing high-quality, reproducible code including unit tests, documentation will be considered an advantage.
Evidence of teaching or mentoring, volunteering, community building will be considered an advantage.
The funds are available from December 2024. We expect to hold interviews on a rolling basis.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
For informal enquiries, please contact Dr Ferenc Huszár: fh277@cam.ac.uk.
You will need to upload a full curriculum vitae (CV) and a 1-page covering letter outlining your relevant past experience, and include the contact details for 2 referees. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.
Please quote reference NR44217 on your application and in any correspondence about this vacancy.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.