Research Fellow / Senior Research Fellow in Artificial Intelligence for Cancer Digital Twins

Cancer Institute Faculty of Medical Sciences

Job Description

Research Fellow / Senior Research

£

• £ Fellow in Artificial Intelligence for Cancer Digital Twins

Department: Research Department of Oncology Grade 7: £43,981- £54,019 per annum Grade 8: 56,431 62,903 per annum Salaries are inclusive of London Allowance

Location: UCL Cancer Institute, London

Reports to

Professor Jasmin Fisher

Context

This post is based in Professor Jasmin Fisher’s laboratory at the UCL Cancer Institute. The UCL Cancer Institute is a world-leading centre for cancer research, bringing together more than 400 scientists and clinicians who work collaboratively to understand cancer and translate discoveries into improved diagnostics, treatments and patient outcomes.

We are seeking a highly motivated and talented Senior Research Fellow in Artificial Intelligence to join an ambitious and multidisciplinary research programme focused on understanding cancer using AI to generate digital tumour twins. This programme aims to transform cancer research and precision oncology by developing a new generation of transparent, interpretable and trustworthy AI technologies that integrate machine learning, mechanistic modelling, formal verification and large-scale biomedical data.

Cancer is a complex, multi-scale disease involving interacting processes across the molecular, cellular, tissue and organ levels. At the same time, advances in genomics, molecular profiling, pathology, imaging and clinical data collection have generated unprecedented volumes of multimodal cancer data. A major challenge is to integrate these diverse data sources into coherent computational frameworks that can generate biological insights, support clinical decision-making and accelerate therapeutic discovery.

The Fisher laboratory addresses this challenge through the development of tumour digital twins, which are executable mechanistic models that capture the biological behaviour of individual tumours and can be used to predict disease progression and treatment response. A central goal of the programme is to develop novel neurosymbolic AI methodologies that combine the power of large language models, and machine learning with formal reasoning, mechanistic modelling and formal verification to automatically

construct and validate transparent, interpretable biologically grounded models directly from large-scale cancer datasets and scientific knowledge.

The successful candidate will play a central role in the design and development of a neurosymbolic AI platform for cancer. They will contribute to the integration and analysis of large-scale multi-omics and clinical cancer datasets, the development of AI models and the creation of explainable and verifiable computational frameworks for tumour digital twins.

formal methods and cancer research, the postholder will collaborate closely also with Professor Mateja Jamnik and her group in the Department of Computer Science and Technology at University of Cambridge, alongside biologists, clinicians and computer scientists from partner organisations.

The research will contribute to fundamental advances in our understanding of cancer biology while supporting the development of innovative approaches for patient stratification, therapeutic target discovery, treatment optimisation and precision oncology. The successful candidate will have the opportunity to publish in leading journals, present their work at major international conferences and contribute to the development of transformative AI technologies with significant scientific and clinical impact.

This is an exceptional opportunity for an ambitious AI researcher who is passionate about applying cutting-edge AI technologies to challenging real-world problems in biomedicine. The position offers the chance to work at the forefront of trustworthy AI, neurosymbolic reasoning, computational biology and cancer systems medicine, within a world-leading research excellence and translational impact.

Appointment at Grade 7 and Grade 8 is dependent upon the successful award of a PhD who have not yet been awarded their PhD may be appointed initially at Research Assistant Grade 6B (with progression to Grade 7 and backdated salary adjustment upon submission of the final corrected PhD thesis.

Appointment at Grade 8 is contingent on the candidate’s previous experience.

For more details about the Fisher Lab, please visit: This post is funded for 3 years in the first instance, with a probationary period of 9 months.

Applications should include a CV and a Cover Letter: Working at the intersection of artificial intelligence, environment committed to scientific

. Candidates www.ucl.ac.uk/cancer/fisher-lab In the Cover Letter, please provide evidence of the essential and desirable criteria in the Person Specification part of the Job Description. (By including a Cover Letter, you can leave blank the 'Why you have applied for this role' field in the application form, which is limited in the number of characters it will allow.)

Cancer Institute Faculty of Medical Sciences

Main purpose of the job

The post-holder will contribute to a major new research programme focused on developing neurosymbolic AI platform and tumour digital twins support the development of personalised therapeutic strategies. The role will involve the integration, curation and analysis of large-scale cancer datasets approaches that combine biological knowledge with advanced AI and formal reasoning methodologies.

Applicants should hold a PhD in Machine Learning, Computer Science or a related quantitative discipline, with extensive experience in machine learning, deep learning models, large language models (LLMs) and foundation models or generative models. Excellent programming skills in Python, PyTorch/TensorFlow are essential. Candidates should have a proven track record of research in the development and application of generative AI methods and advanced machine learning models, including experience in fine-tuning, evaluating and deploying large language models for scientific and real-world applications. Experience with model interpretability, explainable AI (XAI) techniques and the analysis of model behaviour and performance is highly desirable, particularly in the context of complex scientific datasets. Prior knowledge of formal verification techniques, symbolic reasoning and logical modelling approaches would be advantageous. Experience in handling, curating and integrating largescale, heterogeneous datasets for AI applications, together with familiarity with data governance, reproducible machine learning workflows, model evaluation and AI safety considerations, is highly desirable. Excellent analytical, problem-solving and communication skills, along with the ability to work collaboratively within a multidisciplinary research environment, are essential.

The successful candidate will be expected to contribute both independently and collaboratively within a highly interdisciplinary environment that brings together expertise in cancer biology, bioinformatics, machine learning, formal methods and computational biology. The ability to work proactively, solve complex problems and adapt to new challenges is essential.

Candidates must have excellent written and verbal communication skills, including experience presenting complex scientific concepts to a range of audiences and contributing to high-quality scientific publications. They should demonstrate a strong commitment to the Fisher Lab’s mission and values, together with excellent interpersonal and the to multidisciplinary teams.

trustworthy to improve our understanding of cancer and and the development of computational skills ability work effectively within The post-holder will be expected to support other members of the Fisher Lab, collaborate with external academic and clinical partners and contribute to the organisation and smooth running of the laboratory as appropriate.

Duties and responsibilities

The post-holder will be expected to

• Develop and improve machine learning models for tumour digital twins
• Work on foundation models and multimodal AI systems
• Investigate model robustness and generalisation across diverse biomedical datasets
• Contribute to the generation of synthetic biomedical data
• Assist in integrating models into user-facing tools and applications
• Collaborate with machine learning experts, clinicians and biologists to evaluate models in medical and training contexts
• Contribute to open-source software releases and research publications
• Plan, develop and undertake innovative, high-quality research aligned with the objectives of the research programme.
• Develop a neurosymbolic AI platform to support the creation of cancer digital twins
• Design, implement, fine-tune and evaluate AI models, including large language models and foundation models, for biomedical and translational research applications.
• Develop robust computational methods for integrating, analysing and interpreting large-scale, heterogeneous biomedical and multi-omics datasets.
• Contribute to the development of explainable and trustworthy AI approaches, including model interpretability, validation and verification methodologies.
• Ensure the accuracy, integrity, reproducibility, scalability and reliability of all research outputs, software tools and codebases, following best practices in software engineering and research computing.
• Independently drive research projects, establish analysis pipelines, plan workflows, troubleshoot technical challenges and deliver objectives within agreed timelines and milestones.
• Maintain awareness of advances in the scientific literature and emerging technologies relevant to artificial intelligence, computational biology, bioinformatics, systems biology and cancer research.
• Participate actively in scientific discussions, laboratory meetings, journal clubs and collaborative project meetings.
• Collaborate closely with partner organisations and external national and international collaborators.
• Maintain accurate, comprehensive and up-to-date research records in accordance with UCL policies, research integrity standards and best practices in data management.
• Support and advise colleagues on computational, bioinformatics and AI-related aspects of ongoing research projects.
• Analyse research findings and prepare reports, presentations, manuscripts, software documentation and other research outputs.

• Write, contribute to and lead high-quality scientific publications in peer-reviewed journals.
• Contribute to the preparation of research grant applications, fellowship proposals and funding submissions where appropriate.
• Present research findings at internal meetings, national and international conferences, workshops and other scientific forums.
• Communicate scientific progress, technical developments and research outcomes clearly and effectively through written reports and oral presentations to both specialist and non-specialist audiences.
• Attend seminars, workshops, training activities and professional development opportunities relevant to the project and the post-holder’s career progression.
• Contribute to the wider research activities of the Fisher Lab
• Assist in the supervision, training and mentoring of PhD students, research assistants and other junior researchers where appropriate.
• Foster a positive, collaborative and inclusive research culture, providing support to colleagues and contributing to an effective multidisciplinary team environment.
• Participate in laboratory, departmental and institute activities commensurate with the grade of the post.
• Assist with laboratory management, project coordination, data stewardship and administrative responsibilities as required.
• Adhere to all UCL policies and procedures, including those relating to Equality, Diversity and Inclusion, research integrity, data protection, data management, information governance, health and safety and fire regulations.
• Undertake any other duties commensurate with the grade and responsibilities of the post as reasonably required by the Principal Investigator.

General

• Follow and actively promote the UCL Ways of Working.
• Takes responsibility to carry out duties in a way that embodies UCL's environmental and social sustainability values, actively supporting UCL's Sustainability Strategy, policies and objectives within the remit of their role.
• Carry out any other duties within the scope, spirit and purpose of the job as requested by the line manager.
• This job description may be reviewed and be subject to amendment in consultation with the post holder.

Person Specification

Note to job applicant: copy and paste the following criteria into your “Statement in support of your application” and describe underneath each criteria how you meet it, giving examples. You will be scored on how you meet each criteria.

Essential Criteria

Qualifications, experience and knowledge

PhD in Machine Learning, Computer Science or a related field

A strong publication record in relevant venues Excellent programming skills (e.g., Python, PyTorch/TensorFlow) Strong experience in deep learning models, machine learning and LLMs Demonstrated experience with foundation models or generative models and LLM-based applications Strong understanding of the autoformalisation process and the translation of natural language knowledge into formal representations.

Experience with automated theorem proving, symbolic reasoning and related computational logic techniques.

Experience with multimodal AI or LLM integration Experience in handling, curating and analysing large-scale, complex and heterogeneous datasets.

Excellent written and verbal communication skills, with the ability to communicate complex technical concepts to both specialist and nonspecialist audiences.

Ability to work effectively within a multidisciplinary and collaborative research environment.

Skills and Abilities

Excellent computational, analytical and problem-solving skills.

Strong organisational skills, including the maintenance of accurate, traceable and well-documented code, data and research records.

Demonstrated commitment to reproducible and open research practices.

Ability to work independently, take initiative and drive research projects to completion.

Excellent attention to detail and commitment to scientific rigour.

Strong forward-planning, project management and time-management skills, with the ability to prioritise competing demands and meet deadlines.

Ability to critically evaluate research findings and contribute innovative ideas to ongoing projects.

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Ability to collaborate effectively with researchers from diverse scientific backgrounds.

UCL Ways of Working

Taking pride in delivering an excellent service to colleagues, students, partners and the wider community.

Actively seeking solutions to any bullying, harassment and discriminatory behaviour in teams

Desirable Criteria

Knowledge of formal reasoning and verification Experience in applying machine learning to biomedical datasets Experience with neurosymbolic reasoning, neural theorem proving Experience with knowledge graphs, biomedical ontologies and scientific knowledge extraction.

Experience working with large datasets or open-source machine learning frameworks Knowledge of cancer biology, systems biology or computational biology Experience with multi-omics, clinical or biomedical data integration.

Experience contributing to grant applications or collaborative research proposals.

Experience supervising, mentoring or training students and junior researchers.

Experience working within interdisciplinary teams spanning artificial intelligence, computer science and life sciences.

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