AI-Driven Biomolecular Design & Interactions Lecturer

UCL; Greater London; On-site; GBP 60,000 - 90,000; Full time

Benefits offered by this job

41 Days holiday (27 days annual leave

8 bank holidays and 6 closure days)

Relocation scheme for certain posts

On-Site nursery

On-site gym

Cycle to work scheme and season ticket

Immigration loan

Enhanced maternity, paternity andadop

Job summary

UCL's Division of Biosciences invites applications for a Lecturer/Associate Professor in AI for Biomolecular Design & Molecular Interactions. The post focuses on AI-driven prediction, design of biomolecular interactions, and cross-disciplinary collaboration within SMB and the AI for Life programme.

The successful candidate will lead a research and teaching program, develop innovative approaches blending structural biology, biophysics, quantitative biology and machine learning, and contribute to

Qualifications

• PhD or equivalent in a relevant field with an AI-focused research agenda.
• Proven track record in AI-driven biomolecular design and interaction modelling.
• Experience in research-led teaching at undergraduate and/or postgraduate level.
• Ability to build collaborations and attract research funding.

Responsibilities

• Establish or expand a research and teaching programme centred on AI-driven approaches for biomolecular design.
• Develop and deliver teaching across undergraduate and postgraduate levels (computational biology, protein-nucleic acids, and design).
• Foster collaborative partnerships with experimentalists and theoreticians within SMB and beyond.
• Contribute to cross-division initiatives such as MRes AI for Bioscience and secure competitive funding.

Skills

AI for biomolecular design

Computational biology

Machine learning

Biophysics

Biochemistry

Education

PhD in a relevant field

Tools

Python

R

ML frameworks

Job description

UCL's Division of Biosciences invites applications for a Lecturer/Associate Professor in AI for Biomolecular Design & Molecular Interactions. The post focuses on AI-driven prediction, design of biomolecular interactions, and cross-disciplinary collaboration within SMB and the AI for Life programme.

The successful candidate will lead a research and teaching program, develop innovative approaches blending structural biology, biophysics, quantitative biology and machine learning, and contribute to