PhD Programme in Advanced Machine Learning
The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato, Carl Rasmussen, Richard E. Turner, Adrian Weller and Zoubin Ghahramani.
The typical duration of the PhD will be four years.
Applicants must formally apply through the GRADSAF system at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller and/or Ghahrama
Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.
Deadline for PhD Application: 6th December
Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.
Further information about completing the admissions forms:
The Machine Learning Group is based in the Department of Engineering, not Computer Science.
We will assess your application on three criteria:
- Academic performance (make sure to ensure evidence for strong academic achievement e.g. position in year, awards etc.)
- references (clearly your references will need to be strong, they should also mention evidence of excellence as quotes will be drawn from them)
- research (detail your research experience, especially that which relates to machine learning)
You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.
Information about the Cambridge-Tuebingen programme:
We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here. Remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly. Note that the application deadline for the Cambridge-Tuebingen programme is November 12, 2017.
What background do I need?
An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.
Do you have funding?
There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see http://www.admin.cam.ac.uk/
What is my likelihood of being admitted?
Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and have some research experience in statistical machine learning. Other factors that play a role are reference letters, and exam marks (e.g. GRE).
Do I have to contact one of the faculty members first or can I apply formally directly?
It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.
Do you take Masters students, or part-time PhD students?
We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we have just launched a new one-year taught Master’s Programme: The MPhil in Machine Learning, Speech and Language Technologies. You are welcome to apply directly to this.
What Department / course should I indicate on my application form?
This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).
How long does a PhD take?
A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.
What research topics do you have projects on?
We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.
What are the career prospects for PhD students from your group?
Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage. Research expertise in machine learning is in very high demand these days.