Our Offer
Become a future leader in Distributed Algorithms and solve some of the world’s toughest challenges by working at the interface of AI, machine learning and data science. Study with a team of world-leading researchers and develop flexible, fast and efficient data science solutions utilising future computing power.
Why Choose the CDT in Distributed Algorithms?
• Funded PhD studentships
• Generous stipend and additional support for your doctoral studies (rates)
• PhD projects co-defined and co-supervised with a project partner
• Placement opportunities
• Great employability links
• Inclusive and supportive cohort environment
• Technical and Personal training and development
• Access to state-of-the-art computing hardware
Current Opportunities
We are currently interviewing applicants for projects 1-6 (see the timelines below).
In the interim, submit a Generic Application and be notified when new projects are released.
Apply Now
Please follow the DA CDT Application Instructions in and apply online via the University's PhD Application Portal. The DA CDT application instructions will guide you through the University’s generic PhD application form.
Timelines
Applications for October 2021 entry are open. The CDT will consider applications in several rounds until all CDT places have been filled.
| ROUND | 1 | 2 | 3 |
|---|---|---|---|
| Application Deadline | 31 January 2021 | 31 March 2021 | 31 May 2021 |
| Outcome of Interviews Notification Expected | 28 February 2021 | 30 April 2021 | 30 June 2021 |
The DA CDT is committed to providing an inclusive environment in which diverse students can thrive, the CDT particularly encourages applications from women, disabled and Black, Asian and Minority Ethnic (BAME) candidates, who are currently under-represented in the sector.
Eligibility and Funding
The full financial award package includes: the stipend to support living costs, fees up to the UK/Home student fees rate, and additional reserach support costs (for conferences, travel, consumables, etc).
International students (including EU students) wishing to study at the CDT can apply for a studentship. A select number of successful international students will be eligible for entry to the CDT. International studentships are limited to 30 percent of the total cohort (each year) and places will be competitive. We reccomend international students apply early as places are limited. The CDT aims to recruit approximately 15 students each year.
The academic year 21/22 brings changes to EU student's eligibility for all EPSRC-funded CDT studentships. EU and EAA students will become classified as international students if they do not meet the eligibility requirements. Eligibility is based on residency not nationality. Refer to Annex B (pg 21) of this webpage for eligibility criteria.
Applicants should have, or be expected to obtain, a minimum of a 2.1 undergraduate degree and/or Masters qualification or hold exceptional work experience in a related field (Statistics, Data Science, Computer Science, Electrical Engineering, & Electronics, Engineering, Mathematics, Physics, or any other numerate subjects).
It is not necessary that an applicant has completed their current course of study before applying. If an applicant has not completed their current course of study, any offer may be conditional on the eventual degree classification.
We welcome applications from those with non-standard qualifications who can demonstrate knowledge, experience and skills developed in the workplace, or elsewhere, relevant to the the CDT vision. Please use your DA CDT Application Instructions to provide further details.
Other useful information
- Coronavirus (Covid-19) Safety at the University of Liverpool
- Information for Prospective Students on the UK leaving the EU
"I had decided to further my studies in Computer Science. This PhD project gives an excellent opportunity to do some research, work with an industrial partner and learn how to expand the theory and at the same time, apply it to a specific problem." - Current CDT PhD Student
Back to: Centre for Doctoral Training in Distributed Algorithms