We are seeking a Clinical Scientist with a background as a physician-scientist in oncology to join our multidisciplinary Artificial Intelligence and Machine Learning (AIML) team. This position offers a rare opportunity to shape the future of oncology research by directly integrating clinical expertise with pioneering AIML advancements, setting our team apart as leaders in translational medicine.  

The role is uniquely positioned to bridge clinical practice, advanced AIML research, the Oncology Research Unit (ORU), and the Oncology Translational Research Organization (OTRO), with a particular emphasis on leveraging large language models (LLMs) and other emerging AIML methodologies. The successful candidate will be a training fellow, upskilling in AIML while providing essential clinical expertise to drive innovation in drug discovery and development.  

You will benefit from dedicated mentorship and hands-on training in advanced AIML techniques, fostering rapid professional growth at the intersection of clinical science and artificial intelligence. 

In this role you will 

Clinical and Research Integration 

• Translational Research Liaison: Act as the primary conduit for introducing new AIML methodologies to the ORU and OTRO. 

• Collaborate with computational pathology teams to identify AIML opportunities. 

• Work with organoid and patient-derived tissue explant teams to integrate AIML approaches. 

• Partner with spatial transcriptomics and biomarker discovery groups to advance AIML applications. 

• Clinical Prioritisation: Provide clinical guidance to prioritise work within our in-house Digital Biological Twin (DBT) laboratory, ensuring AIML efforts focus on the most pressing clinical questions and therapeutic needs in oncology. 

• Clinical Development Alignment: Collaborate with Oncology Clinical Development Leads to align objectives with strategic clinical development plans, ensuring translational research efforts remain relevant and impactful. 

• Research Clinic Engagement: Dedicate one day per week to attend a research clinic, such as a lung cancer clinic, at a partner translational research network hospital. Contribute to GSK-sponsored studies, providing real-world clinical context to our research. 

AIML and Innovation 

• Novel Use Case Identification: Proactively identify innovative, high-impact AIML applications in oncology, especially through LLMs, multimodal data analysis, and emerging technologies, using your understanding of clinical workflows and unmet needs. 

• Product Scoping: Lead the scoping of new AIML products and projects. Define problem statements, outline clinical and scientific requirements, and collaborate with data scientists and engineers to evaluate feasibility and potential impact. 

• Multimodal Data Interpretation: Work with data scientists to interpret and provide clinical context for AIML-generated insights from diverse data types, including clinical notes, pathology images, genomic data, and patient-reported outcomes. 

• LLM Application and Validation: Guide the application of Large Language Models for oncology use cases such as synthesising scientific literature, summarising clinical trial data, and identifying novel patient cohorts or biomarkers. Provide clinical validation to ensure LLM outputs are accurate, safe, and meaningful. 

• Target Identification & Biomarker Discovery: Partner with target identification and biomarker discovery teams to apply AIML in identifying and validating new therapeutic targets and predictive biomarkers, ensuring clinical utility and biological plausibility. 

• Algorithmic and Methodological Strategy: Provide clinical and scientific input into AIML algorithm and computational pipeline design and strategy. Help define problems and evaluate solutions, ensuring technology is tailored to oncology research needs. 

Why you 

Basic required qualifications 

We are looking for professionals with these required skills to achieve our goal 

• Medical degree and PhD (or equivalent) A combined medical and doctoral degree, demonstrating a strong foundation in both clinical medicine and hypothesis-driven research. 

• Oncology Expertise: Experience in a specific oncology domain. 

• Research Acumen: A strong track record of scientific publication and research in a relevant oncology field. 

Preferred qualifications and skills 

If you have the following characteristics, it would be a plus: 

• Computational Fluency: Solid understanding of data science, machine learning, and large language models. A fast learner with curiosity for new technologies and their clinical applications. 

• Systems Thinking: Ability to approach clinical problems in a structured, data-driven manner and appreciate how AIML can provide end-to-end solutions for improved efficiency and outcomes. 

• Multimodal Data Experience: Familiarity with integrating diverse types of clinical and biological data.