collageMachine learning & AI
Fully funded PhD Scholarship – Machine learning & AI for activity recognition (2019)
A 3-year fully-funded PhD position is available in the Wearable Technologies Lab at the University of Sussex.
Type of award
Postgraduate Research
PhD project
== Objectives ==
AI and machine learning techniques can be used to infer human activities by interpreting the data originating from a variety of multimodal wearable, mobile and ambient sensors.
This can be used to provide contextual assistance with applications in human-robot interaction, healthcare, industrial assistance, sports training, skill assessment, entertainment, and others.
Within this project, you will seek methods to make it easier to train systems to recognise a wider range of human activities. You will research advanced machine learning and AI techniques to recognise a growing set of activities from multimodal sensors, and reduce the effort associated with acquiring annotated training data.
Depending on your interests, different approaches can be followed: deep transfer learning to exploit the growing availability of multimedia datasets (e.g. Google AVA dataset, Youtube data, the Sussex-Huawei dataset), interactive machine learning, crowd-sourcing, adaptive machine learning, and others.
The project can be oriented towards methods achieving high performance for offline usage, or methods suitable for real-time activity recognition running on embedded platorms. Demonstrators arising from this project are welcome.
== About the Lab ==
The Wearable Technologies Lab, led by Dr. Daniel Roggen, has been established in 2014. Since then, it has acquired funding from Google, Huawei, EPSRC, Unilever, the Austrian FFG, and others.
The focus of our lab is to advance AI techniques to automatically recognise and understand human activities or daily routines from wearable and mobile sensors. We have developed several wearable sensing platforms and software frameworks for this, including deep learning and ASIC-friendly approaches.
The lab has created numerous dataset for activity recognition research, the most recent is a massive transportation dataset – the Sussex-Huawei Locomotion dataset (www.shl-dataset.org) – which has been used in two prominent machine learning challenges at Ubicomp 2018 and Ubicomp 2019.
Some of our applications are in the fields of sports performance, industrial assistance, mobility monitoring, crowd behaviour analytics and healthcare.
The members of the lab have an international outlook, with a mix of computer scientists, computer engineers, and electronic engineers.
The lab has state of the art computing and electronics facilities with a wide range of technologies at hand: GPU computing platforms, augmented reality glasses, smartwatches, a vast array of datasets and ad-hoc software tools to support research, numerous novel sensor technologies and sensing platforms, etc.
Amount
£15,009 stipend, plus fees (at the UK/EU rate).
You will receive a tax free stipend at a standard rate of £15,009 per year for three years. In addition, your fees will be waived for three years (at the UK/EU rate).
Overseas applicants are welcome to apply if they can meet the fee shortfall.
Eligibility
Open to candidates from UK/EU countries and from non-EU countries.
The ideal candidates will have a master’s degree in computer science, computer engineering, physics, mathematics, electrical engineering, or equivalent, with prior experience desired in machine learning and ideally in embedded and wearable systems. The candidate will have excellent technical skills, including programming in some of Python, C/C++ or Matlab and experience with deep learning frameworks and Linux.
The ideal candidates will have a passion to contribute to the development of novel wearables which can improve quality of life. They will have outstanding technical skills and a strong interest in research at the crossroads of signal processing, machine learning, embedded systems, sensor technologies and their applications.
Applicants should be committed to pursue leading research and publish results in top venues. Additionally, we expect mastery of written and spoken English, self-motivation, an inquiring mind, be able to work independently and in an interdisciplinary environment.
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, and Adrian Weller. Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD but for further information on our current research areas please consult our webpages at http://mlg.eng.cam.ac.uk.
The typical duration of the PhD will be four years.
Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, and/or Weller). Applicants that want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters) and we will not be able to see any application until that happens.
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: noon December 3, 2019
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. IMPORTANT: 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 3, 2019 11:59pm AOE.
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 https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 5 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!
If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.
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, references, and have some research experience in statistical machine learning.
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 run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence. 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.
Research profile
The PhD in Data Science and Artificial Intelligence is a 3 years PhD programme offered by the School of Informatics. Those on this PhD programme will also engage and interact with students in the School of Mathematics. The PhD follows on from the EPSRC CDT PhD in Data Science.
The PhD recognises that if researchers in this area wish to be leaders in AI and shape the technology landscape, they must be deeply conversant in both data science and AI. Only then will they be able to direct and better understand how to research, develop and target technologies that will be the pioneering breakthroughs.
Data science is the study of the computational principles, methods and systems for extracting knowledge from data. Almost every activity in science, society and commerce now relies on data-driven decision making — from large data analytics in biology to small companies deploying data-collecting apps to millions of customers.
AI is the study of computational systems that demonstrate capabilities of perception, reasoning, learning and action that are typical of human intelligence. We have also recently seen an explosive growth in the capability of modern AI technologies. The great recent successes of modern AI, such as object recognition and game playing, are based on data-driven approaches rooted in machine learning and deep networks.
Our programme will allow you to specialise and perform advanced research at the interface of Data Science and AI and gain breadth and practical experience throughout the field.
You will be supervised by one of our 85 world-renowned faculty members. You will also benefit from interacting with a group of 30+ leading industrial partners, including Microsoft Research, Aberdeen Standard, Amazon, Samsung, Huawei, IBM and IBM Research amongst many others.
*(Revised 9 January 2020 to remove Doctoral Research Centre from name and content.)
Programme structure
You will spend your programme duration developing and pursuing a Data Science and AI research project.
You will be assigned a supervisor, and with their guidance you will undertake independent research and write it up as a research thesis.
Training and support
While the emphasis is on independent research work, your supervisor will identify your training needs and so may encourage you to attend research training classes and/or some taught courses in Informatics.
As part of the PhD programme, you will also have opportunities for three to six month internships with leading companies in your area, and to participate in our industrial engagement programme, exchanging ideas and challenges with our sponsor.
Facilities
Our research groups contain a diverse range of compute clusters for compute and data-intensive work, including a large cluster hosted by the Edinburgh Computer and Data Facility.
More broadly, the award-winning Informatics Forum is an international research facility for computing and related areas. It houses more than 400 research staff and students, providing office, meeting and social spaces.
It also contains two robotics labs, an instrumented multimedia room, eye-tracking and motion capture systems, and a full recording studio amongst other research facilities. Its spectacular atrium plays host to many events, from industry showcases and student hackathons to major research conferences.
Nearby teaching facilities include computer and teaching labs with more than 250 machines, 24-hour access to IT facilities for students, and comprehensive support provided by dedicated computing staff.
Among our entrepreneurial initiatives is Informatics Ventures, set up to support globally ambitious software companies in Scotland and nurture a technology cluster to rival Boston, Pittsburgh, Kyoto and Silicon Valley.
Career opportunities
We intend for our graduates to become the research leaders, both in industry and academia, whose work will lead the way in data science. This vision is shared by our industrial supporters, whose support for our internship programme indicates their strong desire to find highly qualified new employees.
You will be part of a new generation of data scientists, with the technical skills and interdisciplinary awareness to become R&D leaders in this emerging area.
Our component research groups already have excellent track-records in post-graduation destinations, including the research labs of industry-leading companies, and post-doctoral research positions in top tier universities.
Entry requirements
A minimum UK 2:1 honours degree, or its international equivalent, in computer science, mathematics, physics, engineering or a related discipline. Most students accepted to the programme hold a first class degree.
Applicants coming directly from a bachelors or masters degree are welcome.
We particularly welcome applications from members of communities under-represented in computer science and mathematics.
International qualifications
Check whether your international qualifications meet our general entry requirements:
English language requirements
You must demonstrate a level of English language competency at a level that will enable you to succeed in your studies, regardless of your nationality or country of residence.
English language tests
For 2020 entry we accept the following English language qualifications at the grades specified*:
- IELTS Academic: total 6.5 with at least 6.0 in each component.
- TOEFL-iBT (including Special Home Edition): total 92 with at least 20 in each section. We do not accept TOEFL MyBest Score to meet our English language requirements.
- PTE Academic: total 61 with at least 56 in each of the Communicative Skills scores.
- CAE and CPE: total 176 with at least 169 in each paper.
- Trinity ISE: ISE II with distinctions in all four components.
For 2021 entry we will accept the following English language qualifications at the grades specified*:
- IELTS Academic: total 6.5 with at least 6.0 in each component.
- TOEFL-iBT (including Special Home Edition): total 92 with at least 20 in each section.We do not accept TOEFL MyBest Score to meet our English language requirements.
- CAE and CPE: total 176 with at least 169 in each paper.
- Trinity ISE: ISE II with distinctions in all four components.
*(Revised 21 February 2020 to remove PTE Academic from 2021 entry requirements. Revised 21 April 2020 to include TOEFL-iBT Special Home Edition in 2020 and 2021 entry requirements.)
Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS, TOEFL, PTE Academic or Trinity ISE, in which case it must be no more than two years old.
Degrees taught and assessed in English
We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:
We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries.
If you are not a national of a majority English speaking country, then your degree must be no more than three and a half years old at the beginning of your programme of study.
Find out more about our language requirements:
Fees and costs
Read our general information on tuition fees and studying costs:
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