PhD Programme in Advanced Machine Learning: The Cambridge Machine Learning Group (MLG) invites applications for its PhD Programme in Advanced Machine Learning. Exceptional candidates with backgrounds in Mathematics, Physics, Computer Science, Engineering, and related fields are encouraged to apply. The program, led by renowned supervisors, focuses on basic research in machine learning and its scientific applications. For detailed information on research areas, visit MLG’s webpages.
Summary Table:
| Study Area | Advanced Machine Learning |
|---|---|
| Duration | Four years |
| Application Deadline | Noon, 5 December 2023 |
Scholarship Description: The PhD program spans four years and offers candidates the opportunity to conduct basic research in machine learning. Applicants must apply through the University of Cambridge’s Applicant Portal by indicating “PhD in Engineering” and the preferred supervisor(s). Successful applicants receive partial or full funding for the duration of the program.
Eligibility:
- Strong academic background in Mathematics, Physics, Computer Science, or Electrical Engineering
- Research experience in statistical machine learning is advantageous
- Ability to provide evidence of excellence through academic achievements, awards, and strong references
Required Documents:
- CV with academic background and research experience
- Cover letter expressing motivation and research interests
- Copies of transcripts from previous degrees
- Contact details of two referees
- Research proposal (about 2 pages)
- Recent CV
How to Apply: Submit applications through the Applicant Portal by noon, 5 December 2023. Applicants seeking Cambridge Scholarships should reply ‘Yes’ to the relevant question.
Last Date for Apply: Noon, 5 December 2023
Additional Information:
- The Machine Learning Group is in the Department of Engineering.
- The Cambridge-Tuebingen program has a separate application deadline (noon, 5 December 2023, CET).
- Applicants are assessed based on academic performance, references, and research experience.
- Successful candidates are among the top students, possess strong mathematical backgrounds, and demonstrate research experience in statistical machine learning.
Career Prospects: PhD graduates from the group have achieved excellent positions in academia and industry. Explore recent alumni profiles on the MLG webpage.
Note: Contacting faculty members is not necessary for the formal application, but inquiries are welcome. The official application must be submitted before funding deadlines to be considered.
