Postdoc in AI for Biological Data & Governance

University-of-Oxford; Oxford; Hybrid; GBP 40,000 - 49,000; Full time

Benefits offered by this job

Hybrid working

Interdisciplinary team

Silver Athena SWAN

Job summary

University of Oxford invites applications for two Postdoctoral Research Associate positions in AI, based in the Kavli Institute for Nanoscience Discovery, Oxford. The roles involve leading one of two research strands on biological data and AI generalisation and controls, with a focus on data governance and reproducible computational research.

Successful candidates will work in an interdisciplinary team, contribute to publications, and engage in scientific presentations.

Qualifications

• PhD in ML/CS/Statistics/Computational Science or related field is required or near completion.
• Experience designing and conducting independent empirical computational research.
• Strong Python programming skills and experience with training/evaluating ML models.
• Understanding of computational techniques used in generative or engineering biology.

Responsibilities

• Design and conduct reproducible computational experiments to study data governance and AI capabilities.
• Analyse results, develop methods, and contribute to scientific publications/presentations.
• Collaborate within an interdisciplinary research environment and manage independent research activities.

Skills

Python programming

Empirical computational research

Data governance understanding

Statistical analysis

Experimental design

Education

PhD in Machine Learning, Computer Science, Statistics, Computational Science or related area

Close to completion before appointment

Tools

Python

Job description

University of Oxford invites applications for two Postdoctoral Research Associate positions in AI, based in the Kavli Institute for Nanoscience Discovery, Oxford. The roles involve leading one of two research strands on biological data and AI generalisation and controls, with a focus on data governance and reproducible computational research.

Successful candidates will work in an interdisciplinary team, contribute to publications, and engage in scientific presentations.