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Key information

Award:
PhD
Study mode:
Full time
Part time
Duration:
4 years

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.

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 Francesco Belardinelli STAI CDT Co-Director (Imperial)

Dr Elizabeth Black, STAI CDT Co-Director

Professor Alessio Lomuscio, STAI CDT Deputy Director (Imperial)

Professor Michael Luck, STAI CDT Director

Dr Albert Meroño Peñuela, STAI CDT Co-Director

Dr Odinaldo Rodrigues, STAI CDT Co-Director

Professor Alessandra Russo, 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

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

  • Tuition fees: Funded students will have full fees covered at the appropriate rate, whether home or international.
  • Stipend: a tax-free stipend set at the UKRI rate plus London-weighting (for 2022-23, students studying full-time in London receive a stipend of £19,668).
  • RTSG: A generous allowance will be provided for research consumables and additional training, and for attending UK and international conferences.

Who can apply?

Any prospective doctoral student wishing to study at the Centre, including prospective international students, can apply for a UKRI studentship. All UKRI-funded doctoral students will be eligible for the full award – both the stipend to support living costs, and fees at the UK research organisation rate. However, UKRI international studentships are limited to 30 percent of the total cohort and places will be competitive.

A change in UKRI criteria came into force at the start of the 2021 academic year, following the UK’s exit from the EU. For information see Changes to EU and International Eligibility for UKRI funded studentships from Academic Year 2021-22 onwards.

For further guidance on fee status, visit the:

Home Students

Home students will be eligible for a full UKRI award, including fees and stipend. To be classed as a Home student, candidates must meet the following criteria:

  • be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they will be classed as an International student.

International students

International students applying to the Centre are eligible for full UKRI-funded studentships covering fees at the overseas rate and stipend. Studentships for international students are limited in number and competitive, and we encourage strong international candidates to apply.

Part-time students

It is possible to apply to the Centre 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 Centre 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 Centre are required to:

  • Commit a minimum of 50% full-time-equivalent time to their PhD and the CDT programme.
  • Maintain a regular physical presence in the department during normal working hours.
  • Attend all compulsory elements of the Centre, 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 Centre 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 Centre has a Carers’ Fund, which students may apply to in order to cover caring costs incurred by attendance of Centre 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 Centre please send an email to  stai-cdt-admissions@kcl.ac.uk  in advance of the application deadline in order to discuss this. The Centre is unable to consider part-time applications from applicants who do not do this.

Note that the demands of cohort-based training, and the requirement for a minimum of 50% full time equivalent, mean that this programme is unfortunately not suitable for those wishing to combine a part-time PhD with a full-time career role. In this context, we believe it would be very difficult to effectively participate in this programme on a part-time basis if you are working more than 3 days per week and, moreover, we believe that to be successful, you will ideally be working much less than this. If you intend to combine a PhD with paid work that prevents full engagement with the cohort-based training programme offered by our Centre, you may instead wish to consider opportunities on a standard part-time PhD programme. Please see information on the Computer Science Research MPhil/PhD at the Dept of Informatics at King’s or PhDs in the Dept of Computing at Imperial.

Students in full-time employment

Because part-time students are required to study with a minimum of 50% full-time equivalent, the Centre is unfortunately unable to consider part-time applications from applicants in full-time work. Students in full-time employment are also not eligible for a studentship of any kind.

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 Centre please send an email to stai-cdt-admissions@kcl.ac.uk in advance of the application deadline in order to discuss this.

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.

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

  • Tuition fees: Funded students will have full fees covered at the appropriate rate, whether home or international.
  • Stipend: a tax-free stipend set at the UKRI rate plus London-weighting (for 2022-23, students studying full-time in London receive a stipend of £19,668).
  • RTSG: A generous allowance will be provided for research consumables and additional training, and for attending UK and international conferences.

Who can apply?

Any prospective doctoral student wishing to study at the Centre, including prospective international students, can apply for a UKRI studentship. All UKRI-funded doctoral students will be eligible for the full award – both the stipend to support living costs, and fees at the UK research organisation rate. However, UKRI international studentships are limited to 30 percent of the total cohort and places will be competitive.

A change in UKRI criteria came into force at the start of the 2021 academic year, following the UK’s exit from the EU. For information see Changes to EU and International Eligibility for UKRI funded studentships from Academic Year 2021-22 onwards.

For further guidance on fee status, visit the:

Home Students

Home students will be eligible for a full UKRI award, including fees and stipend. To be classed as a Home student, candidates must meet the following criteria:

  • be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they will be classed as an International student.

International students

International students applying to the Centre are eligible for full UKRI-funded studentships covering fees at the overseas rate and stipend. Studentships for international students are limited in number and competitive, and we encourage strong international candidates to apply.

Part-time students

It is possible to apply to the Centre 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 Centre 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 Centre are required to:

  • Commit a minimum of 50% full-time-equivalent time to their PhD and the CDT programme.
  • Maintain a regular physical presence in the department during normal working hours.
  • Attend all compulsory elements of the Centre, 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 Centre 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 Centre has a Carers’ Fund, which students may apply to in order to cover caring costs incurred by attendance of Centre 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 Centre please send an email to  stai-cdt-admissions@kcl.ac.uk  in advance of the application deadline in order to discuss this. The Centre is unable to consider part-time applications from applicants who do not do this.

Note that the demands of cohort-based training, and the requirement for a minimum of 50% full time equivalent, mean that this programme is unfortunately not suitable for those wishing to combine a part-time PhD with a full-time career role. In this context, we believe it would be very difficult to effectively participate in this programme on a part-time basis if you are working more than 3 days per week and, moreover, we believe that to be successful, you will ideally be working much less than this. If you intend to combine a PhD with paid work that prevents full engagement with the cohort-based training programme offered by our Centre, you may instead wish to consider opportunities on a standard part-time PhD programme. Please see information on the Computer Science Research MPhil/PhD at the Dept of Informatics at King’s or PhDs in the Dept of Computing at Imperial.

Students in full-time employment

Because part-time students are required to study with a minimum of 50% full-time equivalent, the Centre is unfortunately unable to consider part-time applications from applicants in full-time work. Students in full-time employment are also not eligible for a studentship of any kind.

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 Centre please send an email to stai-cdt-admissions@kcl.ac.uk in advance of the application deadline in order to discuss this.

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.

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Key information

Award:
PhD
Study mode:
Full time
Part time
Duration:
4 years

Contact us

For more information regarding our courses please contact us using the details below