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UKRI Centre for Doctoral Training: Safe & Trusted AI PhD

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Overview

The UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI) brings together world leading experts from King’s College London and Imperial College London to train a new generation of researchers in safe and trusted artificial intelligence (AI).  

The STAI CDT offers a unique four-year programme, focussed on the use of model-based AI techniques for ensuring the safety and trustworthiness of AI systems. Students will engage in various training activities, alongside their individual PhD project, ensuring that not only are they trained in state-of-the-art AI techniques, but also that they acquire a deep understanding of ethical, societal, and legal implications of AI in a research and industrial setting. Through engagement with the CDT’s diverse range of industrial partners, students will be exposed to the different experiences, challenges, and technical problems involved in both startups and large corporations.

The CDT leadership team is committed to providing an inclusive environment in which diverse students can thrive. Diversity is crucial for enabling world leading research, impact and teaching, and an inclusive environment is vital to allow people to contribute their best.

What is Safe & Trusted AI?

AI technologies are increasingly ubiquitous in modern society, with the potential to fundamentally change all aspects of our lives. While there is great interest in deploying AI in existing and new applications, serious concerns remain about the safety and trustworthiness of current AI technologies. These concerns are well-founded: there is now ample evidence in several application domains (autonomous vehicles, image recognition, etc.) that AI systems may currently be unsafe because of the lack of assurance over their behaviour. Even in areas where AI methods function to high standards of correctness, there remain challenges. AI decisions are often not explained to users, do not always appear to adhere to social norms and conventions, can be distorted by bias in their data or algorithms and, at times, cannot even be understood by their engineers. An AI system is considered to be safe when we can provide some assurance about the correctness of its behaviour, and it is considered to be trusted if the average user can have confidence in the system and its decision making.

Visit the Safe & Trusted AI website for more details. 

Key information

Duration 4 years

Study mode Full-time, Part-time

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Course detail

Description

Run jointly by King's College London and Imperial College London, the CDT focusses on the use of model-based AI techniques for ensuring the safety and trustworthiness of AI systems. Model-based AI techniques provide an explicit language for representing, analysing and reasoning about systems and their behaviours. Models can be verified and solutions based on them can be guaranteed as safe and correct; and models can provide human-understandable explanations and support user collaboration and interaction with AI – key for developing trust in a system.

King’s College London and Imperial College London are renowned for their expertise in model-based AI and host some of the world’s leaders in the area.

The depth and breadth of expertise in model-based AI is complemented with related expertise in related technical areas such as cybersecurity and data science, and with expertise related to the implications and applications of AI in areas such as security studies & defence, business, law, ethics & philosophy, social sciences & digital humanities, natural sciences & medicine. Find out more about our research at King's and at Imperial.

What does the programme cover?

The training programme has been designed with input from the CDT's industrial partners, ensuring that the skills it will develop are relevant and valuable to industry. 

Alongside their individual PhD project, students will engage in diverse training activities in three areas: 

  • Technical skills training 
  • Training in responsible research and innovation for AI 
  • Transferable skills training. 

Typical training activities include: 

  • Technical training in model-based techniques for safe and trusted AI 
  • Interdisciplinary training on responsible research and innovation for AI 
  • Training on the philosophy and ethics of AI 
  • Public engagement training 
  • Entrepreneurial mindset training 
  • A group project, run in collaboration with the CDT’s industrial partners 
  • Regular seminars and masterclasses on broad-ranging topics relevant to the development of STAI 
  • A hackathon, framed around challenges co-developed with the CDT’s industrial partners 
  • Diversity and inclusion training, including mentoring practices, impact of diversity and inclusion on group dynamics, and inclusive strategies for good research practice. 

In addition, we expect students will have the opportunity to apply for an internship at one of the CDT’s partner organisations.

Direction Team

Dr Elizabeth Black, STAI CDT Co-Director

Dr Natalia Criado, STAI CDT Co-Director

Professor Alessio Lomuscio, STAI CDT Deputy Director (Imperial)

Professor Michael Luck, STAI CDT Director

Dr Daniele Magazzeni, STAI CDT Co-Director

Dr Fariba Sadri, STAI CDT Co-Director (Imperial)

Professor Francesca Toni, STAI CDT Co-Director (Imperial)

Engagement and Partners

Engagement with a broad range of non-academic partners is a key component of the UKRI Centre for Doctoral Training (CDT) in Safe and Trusted Artificial Intelligence (STAI). This engagement provides assurance that both the research supported by the CDT and the skills developed in our students will be relevant and valuable to industry and society at large, while also informing and supporting UK industry in producing state-of-the-art safe and trusted AI solutions.

Partners include:

  • Amazon Web Services (UK)
  • Association of Commonwealth Universities
  • British Library
  • Bruno Kessler Foundation FBK
  • BT
  • Codeplay Software Ltd
  • ContactEngine
  • Ernst & Young
  • Five AI Limited
  • GreenShoot Labs
  • hiveonline
  • IBM
  • Mayor's Office for Policing and Crime
  • Norton Rose LLP
  • Ocado Group
  • Royal Mail
  • Samsung
  • Thales Ltd
  • The National Archives
  • University of New South Wales
  • Vodafone

Contact email

stai-cdt@kcl.ac.uk

Entry requirements

Entry requirements

Applicants will normally be expected to have a distinction at MSc level (or equivalent) in computer science or related discipline. However, in exceptional cases we may consider other qualifications (including at undergraduate level) and all applications will be considered on their merit as appropriate to the individual case. Applications from individuals with non-standard backgrounds (e.g. those from industry or returning from a career break) are encouraged, as are applications from women, disabled and Black, Asian and Minority Ethnic (BAME) candidates, who are currently under-represented in the sector. All applicants will need to demonstrate enthusiasm and aptitude for the programme. 

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.  

Applicants must have a good command of English and be able to apply it in an academic environment. Therefore, those who have not been educated in English will usually be required to provide certificated proof of competence in English language before starting their studies. Applicants should have an IELTS Score of 6.5 overall with a minimum of 6.0 in each skill, or a TOEFL iBT score of 92 overall with a minimum of 23 in writing and 20 in each of the other skills. Equivalent language qualifications may also be considered, see Band D of the King’s College London English Language Requirements, and Accepted English Qualifications in the Imperial College London English Language Requirements.

Help and support

If you don't have a suitable qualification for direct entry to a UK university, or if English isn't your first language, our academic preparation courses can help you get ready for study in the UK.

Preparation courses

Fees and funding

The CDT will fund up to 15 studentships each year, depending on the support available. Each studentship will be funded for 4 years. This includes tuition fees, stipend and a Research Training Support Grant (RTSG). 

  • Stipend: Students will receive a tax-free stipend of ca £17,000 per year. 
  • RTSG: A generous allowance will be provided for research consumables and additional training, and for attending UK and international conferences.  

Home & EU students

EU students will be eligible to apply for a fees-only award and, dependent on residency in the UK, may be eligible for a full UKRI award inclusive of stipend if they have lived, worked or studied within the UK for 3 years prior to the funding commencing. For 2020/21 entry, UKRI will guarantee such students funding for the duration of their course, even when/if the United Kingdom leaves the EU during this time. It is expected that there will also be some full studentships available for EU applicants without the residency requirement.

International students

International students are not normally eligible for these studentships. However, the CDT may occasionally fund outstanding international candidates and we encourage strong international candidates to apply.

Part-time students

It is possible to apply to the CDT to study on a part-time basis and we welcome applications from people who are unable to study full-time due to managing, for example, caring responsibilities, a disability or chronic illness. Because of the nature of the CDT and its training programme, the demands on part-time students are somewhat different to those made of part-time students on a standard PhD programme. All part-time students enrolled in the CDT are required to:

• Be physically present in the department during normal working hours for at least two days a week.
• Attend all compulsory elements of the CDT, including all training activities and all cohort building activities. This may sometimes necessitate full-time attendance over a period (for example, full-time attendance at the CDT Summer School will be expected over a 3 – 4 day period), and such activities may fall outside a student’s typical part-time hours. (Note that the CDT has a Carers’ Fund, which students may apply to in order to cover caring costs incurred by attendance of CDT activities that fall outside of normal hours.)

Part-time students will be supported by a pro-rata studentship in line with their mode of registration (assuming eligibility for a studentship as per UKRI Terms and Conditions).
If you are interested in the possibility of part-time study within the CDT please send an email to stai-cdt@kcl.ac.uk in advance of the application deadline in order to discuss this. The CDT is unable to consider part-time applications from applicants who do not do this.

Students in full-time employment

Applicants should note that it would be exceptional for the CDT to enrol a student who is in full-time employment. It is extremely challenging to manage a standard part-time PhD alongside full-time work responsibilities; CDT students will additionally need to commit to the substantial part-time student requirements outlined above. In order to consider an application from a part-time student in full-time employment (not self-employed), the CDT will require the applicant’s employer to provide a signed letter confirming that they understand and accept the commitments associated with the CDT and are supportive of their employee’s application.

Students in full-time employment are not eligible to receive a tax-free stipend. They may be eligible to receive a fees-only award, dependent on residency.

If you are in full-time employment and are interested in the possibility of part-time study within the CDT please send an email to stai-cdt@kcl.ac.uk in advance of the application deadline in order to discuss this.  The CDT is unable to consider part-time applications from applicants who do not do this.

Self-funded students

We also welcome applications from students (Home/EU/International) who have secured their own funding or are in receipt of alternative scholarships. 

If you are a self-funded student and wish to study within the CDT please send an email to stai-cdt@kcl.ac.uk in advance of the application deadline in order to discuss this.

Financial help and support

Visit the fees and funding webpages to find out more about bursaries, scholarships, grants, tuition fees, living expenses, student loans and other financial help available at King's.

Career prospects

By the end of their studies, students will be:
  • Scientific experts in their PhD topic. Students are supervised by world-leaders in various disciplines at King’s and Imperial.
  • Scientifically broad minded. In contrast to typical PhD graduates, our students acquire not only deep expertise in their own PhD topic, but also develop broad and interdisciplinary knowledge relevant to safe and trusted AI.
  • Trained in Responsible Research and Innovation (RRI) for AI. A further key asset of our students is their awareness of the impact AI may have on society, including possibly adverse impact, and their drive to consider potential implications as a fundamental part of the R&D process. 
  • Equipped  to  maximise  the  impact  of  their  research. The  CDT offers  innovative  training  on entrepreneurship and regular engagement activities with our partners,  including at hackathons, masterclasses and other training events. 
  • Able to engage with the public and other stakeholders. The CDT provides public engagement training and experience, training in techniques for value-sensitive and participatory design, as well as experience of engaging with our diverse partners.
  • Predisposed to working in diverse teams and seeking our multiple perspectives. Students are also trained in inclusive practices and the importance of diversity.

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