Aim
This funding opportunity aims to train the next generation of leaders in computational approaches for bioscience research and innovation, including data science, artificial intelligence (AI) and machine learning (ML). It focuses on building researchers with strong quantitative skills (for example, mathematical, computational) and strengthening the pipeline of individuals who are capable of developing innovative, data-driven approaches to bioscience challenges now and in the future.
While skills in areas like data handling, statistics and bioinformatics are a general requirement for most advanced bioscience degrees, this BBSRC Doctoral Focal Award in AI and data science aims to specifically target future talent in relation to the emergence of new computational techniques and approaches in relation to data intensive bioscience.
We expect students to receive training that equips them with deep, hands-on knowledge of the latest developments in computational systems biology, AI, ML, computer science, informatics, and advanced statistics in the context of bioscience research. We also expect students to gain an understanding of core underpinning topics such as responsible AI, software engineering, data management, and the use of high-performance computing platforms.
The need for these critical skills is evident from the BBSRC’s Review of data-intensive bioscience and UKRI’s AI review Transforming our world with AI and further supported by a broad range of external skills focussed reports (both academic- and industry-led) as well as extensive bioscience community feedback.
The overall aims of this funding opportunity are to support a high standard of collaborative research training through:
- delivering world-class doctoral research, training and development within dynamic and supportive research and innovation environments
- advancing current understanding, generating new knowledge, and developing the breadth of expertise in AI and data in biosciences for future economic and societal impact
- supporting capacity building across AI and data science
- involvement of organisations beyond academia in the selection, development and implementation of projects they would like to support and supervise through CASE studentships
- preparing students to follow a diversity of career paths
- supporting a diverse doctoral community, which includes addressing areas of underrepresentation (for example protected characteristics, types of professions, career stage and porosity within the research and innovation system)
This funding opportunity replaces our centres for doctoral training (CDT) scheme.
Scope
In May 2022, UKRI announced its transition to collective talent funding across talent initiatives. Following this, in November 2023 the UKRI Doctoral Investment Framework was launched. This frames doctoral support around two types of awards – doctoral landscape awards and doctoral focal awards.
Doctoral focal awards:
- provide a focus on the advancement of a specific theme or research challenge, or to build capacity in an area where there is a demonstrable skills gap
- address the development of a theme, research challenge or skill that would not be addressed at scale through a landscape award
- facilitate cohort-based and interdisciplinary training both within and beyond council boundaries (same thematic focus but open to a range of disciplines)
- promote collaboration beyond academia within a specific sector
We are looking to support programmes to deliver high quality, innovative and inclusive cohort-based doctoral training with a focus on development and application of advanced computational techniques and new data-centric approaches for the biosciences.
It is expected that students trained through these programmes should be proficient in advanced computational biology, data analysis, and AI/ML approaches. At the end of their studies, students will be able to deploy their skills across a broad range of bioscience research challenges, disciplines and sectors.
Training programmes supported by this funding opportunity will help build capacity in this increasingly important area, addressing the demand for research leaders in computational biology in the UK, and equipping students with highly transferrable AI/ML and data science skills for a range of career paths.
Thematic areas
You should have a clear vision for the area or areas of training you will focus on, be able to articulate the uniqueness of the offering within the UK doctoral training landscape and explain the benefit of a cohort-driven approach to address this need.
Though you are encouraged to consider what is most appropriate for your specific approach, the following are given as examples of broad thematic areas that doctoral training could be provided in:
- design and use of AI for biosciences, exploring the potential to alter the way research and innovation are performed, through automation (for example, of hypothesis generation, experimental design, or the experiments themselves), new ways of processing, accessing, analysing and generating data
- design and use of advanced computational approaches, enabling quantitative biology at scale, multi-modal and multi-scale data integration and analysis, and advanced modelling approaches
- software engineering for data-centric bioscience research
- development of tools and algorithms for effective use of high-performance computing platforms (for example, distributed computing clusters, Cloud compute, and GPUs) in the biosciences
- advancing the development of AI and related technical standards, guidelines, and related tools that promote innovation and trust
- FAIR data and metadata management in/for the biosciences
Please note that inclusion of AI/ML as a thematic area is mandatory for all applications.
Applications are expected to cover a range of basic and applied bioscience challenges within your chosen thematic area or areas and be within BBSRC remit, rather than exclusively on a singular topic. This should be reflected in the diversity of the supervisory pool and inclusion of relevant bioscience training for students with a background outside of the biosciences in the training offering.
Responsible AI
All applications should consider the implications of deploying AI and other advanced computational approaches in the intended domains, examining the legal, ethical and socio-economic consequences of potentially disruptive AI technologies before they are deployed.
Training remit
Training provided by this funding opportunity must be relevant to the ambitions and challenges of the scope and thematic areas of AI and data science as outlined above. This training should contribute towards building a strong interdisciplinary community of researchers which will bring environmental, economic and societal benefits.
Applications must outline a coherent training programme through which students will both undertake individual research projects, and receive cohort-level training, in cross-cutting skills relevant to the funding opportunity scope.
It is down to you to design and justify an appropriate doctoral training programme to be supported by this funding opportunity. There is no expectation that all students within an application should receive training in all areas or that a single application will address every challenge within this thematic area.
Training delivered by this funding opportunity may build on existing infrastructure where applicable, and engagement with other relevant research council investments, for example, existing doctoral training programmes and their end-user networks, is encouraged.
You are encouraged to consider how you may support a translational approach to AI and data in the biosciences by engaging effectively with partners both within and beyond academia to unite around common challenges.
Training should also be delivered across underpinning enablers such as standards and metrology, responsible research and innovation (RRI) and trusted research and innovation (TRI).
Funding opportunity specific training requirements
Your application must demonstrate that your programme will offer a scientifically excellent training environment and has sufficient high quality research capacity to deliver training across the thematic areas of this funding opportunity. We welcome applications describing innovative models of delivering doctoral training with partners beyond academia, including the co-creation of projects, part-time training and additional training elements designed to meet the objectives of the programme.
You will be expected to deliver leading edge, frontier research and innovation training across the training remit and scope of the funding opportunity.
In addition to the research training remit defined above, there are a number of transferable, professional, technical and personal development training requirements that must be delivered by your programme, these are:
- all students must undertake a placement of a minimum of three months during their studies and training programmes should be designed to facilitate this. See the section on placements
- students should receive explicit careers training and continuous professional development relevant career trajectories within and beyond academia. This must occur early enough to enable students to use it to inform their choice of subsequent training opportunities
- developing awareness of broader issues around AI and data research, for example data curation best practice including the FAIR principles, Trusted and Responsible Research and Innovation practices and policy regulation
- professional skills, and innovation or translation training and collaborations with organisations such as businesses and PSREs for practical exposure and commercial awareness
- development of commercialisation and entrepreneurial skills
- a robust plan for equality, diversity, and inclusion for all students, staff and supervisory teams associated with your programme. See the section on EDI
- clear guidance and training on mental health awareness as part of your induction processes for staff and students, outlining how to access support for mental health and demonstrating how this will be implemented and managed across your partnership
- training needs analysis or equivalent for all students at the outset of training, and the opportunity for students to discuss individual training requirements throughout their studies
- opportunities for students and staff to network across other relevant doctoral training programmes to gain a multidisciplinary perspective
Your application must clearly state how the funding opportunity specific requirements will be delivered as part of your application.
Funding available
Payment will be on a notional studentship basis. We reserve the right to adjust the number of studentships to meet the requirements of the funding opportunity.
There is funding available for up to a maximum of 20 notional studentships per intake over the course of three annual intakes. This funding opportunity will support notional studentships which are four years in length, during which students must complete a placement which is a minimum of three months. We welcome applications which demonstrate interdisciplinarity and multidisciplinary training programmes.
We are expecting to fund a minimum of one and a maximum of two doctoral focal awards on AI and data in the biosciences. The final decision will depend on the number and quality of applications received via this funding opportunity and may also be dependent upon the applications received via the BBSRC Focal Award in engineering biology, which is being delivered in collaboration with Natural Environment Research Council (NERC) and Medical Research Council (MRC).
We encourage you to utilise funding from this funding opportunity to leverage additional investment (either as cash or in-kind support) from multiple stakeholders where appropriate. We also support co-funding to be used from non-research council sources to part-fund additional studentships. However, there is no mandatory requirement for match-funded studentships or cash leverage for this funding opportunity.
You will need to state how many students you wish to support via this award, where payment will be on a notional studentship basis. Please see the ‘Cohort management’ section. We reserve the right to adjust these numbers to meet the requirements of the funding opportunity and to balance our overall studentship portfolio.
A notional studentship consists of sufficient funds to meet the annual UKRI minimum stipend and fee levels, plus additional research, placement and management costs. Notional studentships will be supplemented with London allowance, where eligible.
The student stipend and fees are indicative estimates only, based on the 2023 to 2024 UKRI minima multiplied by four, and excluding London allowance. At the time of award, stipend and fees will be indexed to accommodate rises in the minimum stipend and fees levels over the lifetime of the award. The indicative estimate funding per notional studentship is provided as:
Stipend: £74,488
Fees: £18,848
Research Training Support Grant (RTSG): £20,000
Programme management: £2,000
Total: £115,336
The programme management header above can be used as a contribution towards placements, conferences, and administrative costs. A contribution towards operational management costs has been included within the above indicative funding calculation in recognition of the need to manage the partnership.
We acknowledge that this does not reflect the full cost of landscape doctoral training programme administrative structures. In line with the requirements in the management section, adequate funds must therefore be committed by you from either flexibility within the training grant, leveraged support, or a combination of sources. Training grant funds are not intended to relieve organisations of any part of their normal expenditure.
If successful you will have flexibility in how you use the funding awarded and we encourage flexibility and virement between headings, subject to the standard UKRI terms and conditions of training grants. Be aware that the minimum numbers of students will still need to be supported each year.
To be classed as a BBSRC student, that student must be funded at least 50% by BBSRC. We support co-funding to be used from non-research council sources to part-fund additional students. The details of these students can be registered for reporting purposes.
We welcome proposals which use this investment to leverage additional funding from other sources. The leverage of funding must be in line with the scope of the funding opportunity and under our remit.
Cohort management
Training as part of a cohort is a highly effective way to ensure that doctoral candidates have opportunities to collaborate, exchange ideas and benefit from peer-to-peer support.
You are expected to outline your strategy for developing a cohort identity across the lifetime of their training programme.
For this funding opportunity, we expect you to support a minimum cohort size of five studentships per year, equating to a minimum of 15 studentships total across the lifetime of the award.
There is no upper limit to the number of studentships you may request when applying to this funding opportunity. However, you should take into consideration the number of studentships available for this funding opportunity when stating your request, which is detailed in the “Funding available” section. We expect you to consider the cohort size you can accommodate in relation to the training and experiential needs of your students.
Alignment with UKRI core offer
The UKRI core offer sets out the Statement of expectations for doctoral training for all UKRI studentships, including support and student experience, research skills and methods and professional and career development. All applications must clearly state how the requirements outlined within the UKRI core offer will be delivered as part of their application.
UKRI good practice principles in recruitment and training at a doctoral level
Applications should also demonstrate how they will deliver UKRI good practice principles in recruitment and training at a doctoral level. These principles aim to make the doctoral pathway accessible and attractive to a diversity of potential applicants and outline good practice principles in EDI across the following four key stages of the doctoral recruitment and training process:
- finding talent: to make the doctoral pathway accessible and attract potential applicants who may not currently view a PhD as accessible to them
- shortlisting and interviews: to ensure the applicant shortlisting and interview process is fair and transparent
- nurturing talent: to make the student training experience as inclusive as possible
- monitoring and reporting: to be used effectively to foster a diverse and inclusive environment
Equality, diversity and inclusion (EDI)
EDI is a core feature of this funding opportunity. In line with UKRI’s principles on EDI, we want to work with our partners to shape a dynamic, diverse, and inclusive system of research and innovation that is an integral part of society.
Your doctoral training programme should work to provide everyone involved with an opportunity to participate in, and benefit from, the award.
You must demonstrate how your EDI strategy will embed the core principles of EDI at all levels and across all aspects of the award, including:
- increasing PhD access, including recruitment
- working practices, including individualised student support
- wellbeing support, including mental health
- monitoring and evaluation, including a baseline and plans for improvement
We would expect your EDI strategy to describe how your doctoral programme is accessible to a diverse range of people and needs, and how you will be removing barriers to participation across your doctoral programme and associated processes. Your application should demonstrate how you will create and maintain a positive, inclusive, and supportive environment for all students and staff.
You should refer to equality, diversity and inclusion at UKRI and BBSRC’s equality, diversity and inclusion action plan.
As a mandatory requirement, the EDI strategies, activities and commitments stated by successful applicants will be regularly reviewed by us , including but not limited to, information on characteristics of current and prospective student cohorts. This data will be collected on at least a yearly basis via annual reports, and we will reserve the right to access these data across the lifetime of the award. See the UKRI data collection policy for more information.
Consortia
You may apply as a single-institution or as a multi-institution consortium. Applications are welcome to include a wide range of organisations contributing to AI and data in bioscience research, including, but not limited to:
- industry
- business
- small and medium-sized enterprises (SMEs)
- public organisations
- third sector organisations
- charities
- museums
There is no stipulation regarding the size or geographical spread of consortia applying to this funding opportunity. Within your consortia you must name the organisation which will act as your project lead. See the Who can apply section for eligibility criteria.
All members of your consortia will need to demonstrate that there is significant added value from their inclusion. This may include, but is not limited to, in-kind or financial commitments such as: underwriting a proportion of studentship placements, a commitment to the provision of access to facilities or training that cannot be otherwise provided by another organisation and strategic links to an important stakeholder or user.
Partnerships must show a clear and joint strategy for delivering their vision and fostering the growth and maturation of collaborations over the funding period. Successful applications should demonstrate how students will benefit from engaging with various organisations, both individually and as part of a cohort, utilising diverse mechanisms. Applications must also justify their structure, providing a clear case for the given size of their consortia.
At the time of application, collaborative agreements should be in place regarding management of the doctoral candidate’s work, and agreements concerning any intellectual property that may arise as a result. This collaboration should be effectively managed, to maximise the benefits for all parties concerned.
Management and governance
Multi-organisation consortia will be expected to describe the governance arrangements that will enable effective decision-making and engagement with all relevant stakeholders to achieve the vision of their training programme.
Prospective applicants must commit to providing sufficient support for appropriate administrative resources if they are successful. Applications should explicitly outline how administrative structures will be managed and funded. This funding opportunity will provide funding which can be allocated for programme management including administration and placement support.
CASE studentships and collaborative studentships
CASE studentships are delivered in collaboration with partners beyond academia and must meet the following requirements:
- over the lifetime of the award, a minimum of 25% of the total notional studentships supported by this funding opportunity must be CASE studentships
- successful applicants’ CASE compliance will be reviewed throughout the lifetime of the grant via reporting processes and reserve the right to use the outcomes to adjust future studentship cohort allocations
- the CASE partner or partners must host the student for between three and eighteen months during their PhD. This placement does not need to occur in one single continuous period
- CASE partners are encouraged to participate in the co-creation of studentships projects
- CASE partners must provide co-supervision for any studentship project they are involved with
CASE partners are also strongly encouraged to make a financial contribution to any studentship projects they are involved with, including:
- any costs incurred by the student when visiting and working within their establishment, for example, travel and accommodation costs
- costs of consumables
- facilities and training not possessed by the research organisation that are integral to the CASE studentship
CASE partner eligibility:
- organisations eligible for funding from any UKRI Council (excluding Innovate UK) cannot act as a CASE partner
- international CASE partners are eligible provided they are a non-academic organisation. The training grant holder must evidence that the CASE criteria has been met and that the placement provides an opportunity for the student to gain skills that could not be provided by a UK-based partner. The student must be fully supported by the training partnership and CASE partner throughout the placement period
- public sector research establishments (PSREs) are eligible to act as a CASE partner if they are not already named as a project lead or project co-lead on your application
You must demonstrate within your application the mechanisms you will use to ensure the CASE conversion requirement is met.
In addition to CASE studentships, any number of studentships may be considered ‘collaborative’, for example, where organisations do not meet the requirements for CASE but are still involved in the co-creation or supervision of a students’ research. These collaborative studentships can be reported to your funder and will be formally recognised as a success metric as part of the ongoing monitoring of the awards.
Placements
Placements are a key feature of this funding opportunity, and we expect all doctoral students to undertake a placement. The aim of the placement is to expose students to diverse work environments beyond their academic environment, PhD project, or both. This cultivates transferable skills, enhances their understanding of a variety of career paths and contributes to their personal and professional development.
All students must undertake a placement which lasts a minimum of three months. This placement does not need to occur in one single continuous period. Students have the option of undertaking:
- a Professional Internships for PhD students (PIPS) type placement, where they work outside of academia and conduct work outside of their research project for a minimum of three months. See evaluation of the PIPS programme
- an internship with a project partner, where they work on their research project outside of their academic host setting for between three and 18 months, for example, CASE studentships
- a combination approach, where the student spends part of their placement time on their research project but outside of their academic host setting, for example, CASE studentships, and the other part of their placement time also undertaking a PIPS-type placement
If the student chooses a combination approach, the PIPS element of their placement should be a maximum of three months, whereas the time spent on their research project outside of their academic host setting, for example, with a CASE partner, can be up to 18 months.
Alternative doctorate models, such as professional doctorates, PhDs by Portfolio, and industrial doctorates are permitted. For industrial doctorates, in which students spend most of their time in the industrial setting, this is permitted to count as placement time.
Regardless of the type of placement, all students must be fully supported by their placement host organisation and their training programme throughout the placement period. All placements should be developed in collaboration between the partners across the consortia where relevant and doctoral candidates should have an opportunity to have input on their placement.
Placements are permitted to be based overseas. All costs associated any placement should be met by the placement host. This includes expenses such as the cost of travel, accommodation costs, consumables used on placement and any additional expenses incurred by the student as a direct result of attendance at the premises of the host during the student’s placement period.
Legacy and impact
Training programmes which are supported by this funding opportunity are done so with the intention of developing a legacy of training excellence. Applications must demonstrate consideration of the legacy and impacts of the doctoral training programme beyond the lifetime of UKRI investment.
Reporting requirements and monitoring
The monitoring of progress towards the vision and objectives of your training programme, as well as evidencing of impact, are important components of this funding opportunity. This information will be used by us to review the success of our training investments. Information provided will also be used to provide assurance that the focal wards are being managed appropriately and are progressing in accordance with the original funding application. This will be conducted in various ways, including:
- mandatory annual reports
- a mid-term review of progress
- hosting a regular visit by UKRI staff
Successful applicants will be expected to respond to other reporting requirements when requested.
We will describe the key information required from focal awards in annual reports. This will include diversity statistics for doctoral candidate recruitment, CASE studentships and other collaborative partner engagement, financial leveraging, training, and development activities offered, and examples of doctoral candidate achievements.
You are expected to describe your approach to monitoring and evaluation, outlining their success measures and baselines and a continuous improvement process built in within their applications.
We will oversee and engage with successful applicants to support the delivery of excellent doctoral training.
Flexible fund
This funding opportunity will include an additional flexible fund which will be split across successful applicants. We will award this fund on a per student basis, at an approximate value of £1,750 per student per year of cohort intake for the programme. There will be three consecutive years of intake, starting in October 2026.
The flexible fund will be awarded on a bi-annual basis as an additional funding stream. The exact proportion of this fund will be determined by the notional number of studentships per training programme per year.
The flexible fund can be used to support a range of activities, including support for skills development, network building, or addressing EDI challenges (see relevant EDI sections). Some examples of how these funds can be used is provided in the Flexible fund question section.
Other research staff
UKRI recognises the importance of digital research technical professionals, research software engineers and support staff in the development and deployment of data science, software and AI technologies and ensuring that they are developed in a way which is open and provides broad access to the technology in the public good.
As such, you should consider how you will embed the principle of software sustainability into training and research projects. In addition, you should consider where training packages may be available in your programme which will support associated and aligned research software engineers, technicians and other support staff, or elsewhere in the academic or user training environment.
Duration
This award will support three years’ worth of student intake, starting in October 2026. Each studentship will last for four years, meaning that the total duration of the training grant will be six years (72 months).
Supporting skills and talent
If applicable to your application, we encourage you to follow the principles of the Concordat to Support the Career Development of Researchers and the Technician Commitment.
Trusted Research and Innovation (TR&I)
UKRI is committed in ensuring that effective international collaboration in research and innovation takes place with integrity and within strong ethical frameworks. Trusted Research and Innovation (TR&I) is a UKRI work programme designed to help protect all those working in our thriving and collaborative international sector by enabling partnerships to be as open as possible, and as secure as necessary. Our TR&I Principles set out UKRI’s expectations of organisations funded by UKRI in relation to due diligence for international collaboration.
As such, applicants for UKRI funding may be asked to demonstrate how their proposed projects will comply with our approach and expectation towards TR&I, identifying potential risks and the relevant controls you will put in place to help proportionately reduce these risks.
See further guidance and information about TR&I, including where applicants can find additional support.
