Proposal, prototyping and development of innovative ML models to problems in drug development and computational biology.
Working closely with bioinformaticians and research team to review and translate ML methods into meaningful insights for drug development and computational biology.
Thought leadership and strategy on ML as applied to drug discovery: reviewing state of the art literature in Graph ML, NLP, and Deep Learning and beyond.
Publishing non-confidential material in international conferences and journals.
Exploring collaboration opportunities with world class academics and industrial partners.
Mentoring, management, and growth of ML team.
Working closely with engineering to scale up and optimise pipelines.
Track record of high impact publications in ML. Ideally including graphML and biological data of some form, e.g., omics/interaction networks.
Acceptance of work in top conferences (NeurIPS, ICML, ICLR, ISMB, ECCB ...).
Industry experience (especially in graph learning) is preferred though not essential.
3+ years Python programming experience.
Experience with various deep learning libraries, e.g. PyTorch, Tensorflow.
Experience with graph-specific ML libraries, e.g. DGL, PyTorch Geometric.
Experience implementing industry standard software engineering practices is preferred but not essential.
Communication & Interpersonal skills:
Ability to communicate complex models and data analysis into straightforward results.
Track record of collaboration in research groups across disciplines.
Inclination to manage, grow and inspire teams.
PhD in computer science with a graph-ML focus.
Strong coding abilities, primarily Python.
Strong publication record.