Job Description

About us:
PharosAI offers a unique cancer AI product development ecosystem for drug discovery and clinical applications, democratising access to data, AI models, technologies, and capabilities. PharosAI unites large-scale multimodal cancer datasets with AI models through a highly-secure, trusted, federated platform, offering state-of-the-art AI tooling for use by pharma/biotech/life-sciences, AI-pharma, and AI developers of clinical applications, in enterprises, growth companies, and research organisations.
PharosAI will revolutionise AI-powered cancer care, driving breakthrough therapies, clinical applications, addressing cancer's social determinants, lowering barriers to UK SMEs, catalysing innovation and positioning the UK as a global leader in the dynamic healthcare and AI ecosystem.
About the role:
Overview of the role
We are seeking a highly motivated Machine Learning Research Scientist to join the PharosAI team. This role is focused on developing and implementing novel methods for federated learning in Cancer AI, with a particular emphasis on its application to multimodal data. The position will investigate open research questions and technical challenges in federated and decentralised AI for cancer research. This includes its application to modalities such as pathology, genomics, transcriptomics (spatial biology), proteomics, and electronic health records (EHRs).
For Grade 8: managerial responsibility to supervise researchers (e.g. post-docs, PhD students) and lead research projects/programs.
This is a full time (40 Hours per week), and you will be offered a fixed term contract until 31-03-2024.
About you:
(the candidate)
To be successful in this role, we are looking for candidates to have the following skills and experience:
Essential criteria * PhD in machine learning, computer science, physics, statistics, mathematics or related field.

• Demonstrated developing and implementing federated learning methods (such as optimization & aggregation, privacy techniques and personalisation) with a focus on healthcare and/or biology and modalities such as computational pathology, genomics, transcriptomics or medical imaging.
• Strong experience with Python and at least one deep learning frameworks such as PyTorch, TensorFlow, JAX, and PyTorch Lightning. Familiarity with packages and technologies such as NumPy, Pandas, Scikit-learn, Scikit-image, OpenCV, Git, and Bash.
• Experience working with HPC clusters (e.g. SLURM) or with cloud technologies such as AWS, Azure, or GCP.
• Experience working with federated learning frameworks such as Flower, NVFlare and OpenFL.
• Evidence of high-impact, peer-reviewed publications and experience presenting at academic/scientific/commercial conferences.
• For Grade 8: ability to manage and supervise researchers (e.g. post-docs, PhD students) and lead research programs.

Desirable criteria * Contributions to open-source federated research tooling e.g. NVFlare, Flower, OpenFL and biomedical open-source frameworks such as MONAI.

• Exposure to MLOps frameworks (e.g., MLflow, Kubeflow, Metaflow).
• Experience developing multimodal ML (e.g. early/mid/late fusion) approaches for healthcare and/or biology e.g. computational pathology, genomics, transcriptomics and medical imaging.
• Experience with containerisation and orchestration tools such as Docker, Singularity and Kubernetes.
• Experience working in multi-disciplinary teams with clinicians or life scientists.

Downloading a copy of our
Full details of the role and the skills, knowledge and experience required can be found in the document, provided at the bottom of the next page after you click the Apply button, above. This document will provide information of what criteria will be assessed at each stage of the recruitment process.
We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.
This post is subject to Disclosure and Barring Service and/or Occupational Health clearances.
364,139 to 373,529 per annum inclusive of London Weighting Allowance '