Research Assistant in Compression for Communication-Efficient Training (Fixed Term)
The Department of Computer Science and Technology is a growing academic department within the University of Cambridge that encompasses Computer Science, along with many aspects of technology, engineering and mathematics. You would be working with world-leading computer science researchers in computer architecture, systems, cybersecurity, machine learning and many other topic, each with interesting and challenging requirements.
Applications are invited for a Research Assistant (RA) to join the Cambridge Machine Learning Systems Lab (CaMLSys) in the Department of Computer Science and Technology, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on decentralised training methods for large-scale foundation models.
The role will focus on addressing key technical challenges in decentralised training, including reducing communication and memory overheads while maintaining model performance.
Qualifications/Skills
- Bachelor's or Master's degree in Computer Science, Machine Learning or related field, or equivalent experience.
- Programming proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow).
- Solid understanding of distributed systems or federated learning.
- Strong communication and interpersonal skills.
- Knowledge of privacy-preserving ML techniques.
- Exposure to large-scale system designs or cloud/edge ML systems.
The ideal candidate will be self-motivated, solutions-oriented, and have a solid understanding of decentralized training.
Fixed-term: The funds for this post are available for 3 years in the first instance.
Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.
Applicants should contact Prof Nicholas Lane for further information. https://mlsys.cst.cam.ac.uk/
Please quote reference NR46699 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.