Job Description

Job Information Organisation/Company
KINGS COLLEGE LONDON Research Field
Computer science
Mathematics
Physics Researcher Profile
First Stage Researcher (R1) Country
United Kingdom Application Deadline
15 Jun 2025 - 00:00 (UTC) Type of Contract
Other Job Status
Full-time Is the job funded through the EU Research Framework Programme?
Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure?
No
Offer 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).
The role will also explore and address specific challenges arising from the multimodal nature of the data, such as data heterogeneity and hidden correlation structures, which can significantly impact both learning effectiveness and the design and robustness of privacy-preserving techniques. These techniques include, but are not limited to, differential privacy and homomorphic encryption.
The candidate will contribute to the development of a federated learning platform within the PharosAI infrastructure, working closely with engineering teams. The ideal candidate will have a strong passion for research, software development and a meticulous approach to technical challenges, and the ability to work independently and collaboratively to drive projects to completion.
The candidate will Lead the development of novel machine learning research methods in federated learning with a focus on multimodal cancer data, building on widely adopted algorithms such as FedAvg, FedProx, FedNova, Scaffold, and FedOpt to tackle challenges arising from the multimodal nature of cancer data. This includes data heterogeneity and hidden correlation structures, and their impact on learning performance. Implement and extend privacy-preserving techniques, particularly in the context of multimodal algorithms, e.g. differential privacy, secure aggregation and homomorphic encryption.
Evaluate current opensource federated learning frameworks including NVFlare, Flower and OpenFL and contribute to their development with new methods for multimodal cancer data. Contribute to and support engineering teams in delivering a robust, production-ready federated platform for PharosAI. Support the engineering teams in the development and maintenance of MLOps pipelines to enable continuous integration, testing, deployment, and monitoring of machine learning models across the PharosAI platform. Develop and contribute to open-source projects aimed at the biomedical and healthcare research communities, extending toolkits such as MONAI to computational pathology and spatial biology. Publish research in high-impact, peer-reviewed conferences/journals. Present findings internally and externally at academic and professional conferences. Collaborate with a multidisciplinary team of engineers, data scientists, clinicians and biobankers. 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 an indefinite a fixed term contract until 31-03-2024.
About You
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 Apply Now. This document will provide information of what criteria will be assessed at each stage of the recruitment process.
Further Information
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. We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's.
We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.
To find out how our managers will review your application, please take a look at our ' ' pages.
Where to apply Website
Requirements
Additional Information
Work Location(s)
Number of offers available 1 Company/Institute KINGS COLLEGE LONDON Country United Kingdom City London (Greater) (GB)
Contact City
London (Greater) (GB)
STATUS: EXPIRED
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