ENAIBLE - Enabling Next-generation AI for a Bioscience Led Economy
A four-year doctoral degree uniting world-leading AI research and bioscience innovation.
Applications are still open. Up to a week's notice of closure will be provided on this page - no other notification will be given.
- Expected length:
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- Full time: 4 years
- Expected start date:
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- Full time:
- English language level:
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- Higher level required
About the course
Only applications from applicants who are eligible for Home fees will currently be considered for this course.
ENAIBLE prepares researchers for an AI-driven era of biology ("Biology 2.0"), in which computational systems are active partners in scientific discovery.
Across the UK, bioscience is one of the most strategically important sectors of the economy. Advances in AI are transforming how discovery happens, from hypothesis generation to modelling, experimentation and translation.
ENAIBLE tackles a critical UK skills gap: developing advanced AI and data-science capacity for the £100 billion bioscience sector.
The course will equip you with the technical depth, biological understanding and ethical awareness required to lead in AI-first bioscience. Supported by the Biotechnology and Biological Sciences Research Council (BBSRC), part of UK Research and Innovation (UKRI), ENAIBLE is building national capability in AI-driven, data-intensive bioscience by investing in your doctoral training.
Course structure
Year 1 is an intensive, cohort-based foundation delivered through Oxford’s interdisciplinary Doctoral Training Centre (DTC) and associated departments.
The first 6 months will be full-time training in the form of lectures, classes and practical sessions.
Each student will develop an Individual Training and Development Plan guided by an ENAIBLE Director, ensuring a tailored approach to their academic and professional development.
The modules you will take will vary depending on your research path and existing skills. Options may include topics such as:
- Biosciences data and experimentation
- Software engineering: best practices and sustainable research
- Best practice bioscience data management
- Foundations of statistical inference
- Introductory machine learning for the biosciences
- Responsible AI
- Bioethics
- Advanced deep learning and reinforcement learning for the biosciences
- Mathematical modelling in the biosciences
- AI-enabled modelling and simulation for the biosciences
- Life skills
- Introduction to team projects
In the second part of the first year, you will conduct two three-month rotation projects in different areas, either of which could turn into a full DPhil. This gives you a chance to try new areas of research before you commit to a three-year doctorate.
By the end of Year 1, you will transition from broad exploration to a clearly defined doctoral research direction.
Years 2–4 are dedicated to an ambitious, original DPhil research project.
You will be supported by cross-institutional supervision and ongoing professional development. Students are likely to undertake an industry placement as part of the course.
Research areas
You will have the opportunity to undertake research within the specialised themes of this course.
Course details
Entry requirements
For entry in 2026-27