Research Scientist (Machine Learning) (m/f/d) - Virtual Patient Engine (VPE)

Job Offer #2026-BMX-J02 open until June 5, 2026

Research Scientist (Machine Learning) (m/f/d) - Virtual Patient Engine (VPE)

18 days left to apply!

About BioMed X

BioMed X is a leading innovation hub for pharma. With our global network of research institutes at top universities and incubators within pharmaceutical companies, we bridge the gap between academia and industry.

We are pioneers in applying the principles of design thinking and crowdsourcing to drug discovery and development. With our unique model, we identify key challenges across all therapeutic areas, recruit top academic talent, and co-create tailored solutions with our pharma partners.

In our stimulating ecosystem, early-career researchers grow into future scientific leaders. They perform exploratory research, deliver industry-grade results, and pave the way to next-generation therapies. At BioMed X, we value curiosity, diversity, and purpose. Our goal is to serve as a vital catalyst for innovation in global health for the benefit of all patients.

About Team VPE

The goal of team Next Generation Virtual Patient Engine for Clinical Translation of Drug Candidates (VPE) is to develop a versatile computational platform that can predict the efficacy of first- or best-in-class drug candidates in virtual patient populations at an unprecedented accuracy, thereby addressing one of the most critical bottlenecks of the pharmaceutical industry today: a 90% failure rate of new drug candidates during clinical development. In partnership with Sanofi the VPE team will develop innovative artificial intelligence methods to build the virtual patient platform. As a proof-of-concept, the initial platform will focus on chronic immune-mediated diseases such inflammatory bowel disease (IBD), where new medication that can address patient heterogeneity is needed.

The Position

We are looking for a talented and curious Research Scientist (Machine Learning) to join our team, bringing fresh perspectives and advanced expertise to fuel innovative thinking and scientific excellence. If you are passionate about transforming biomedical data into actionable knowledge within a collaborative environment, this position is for you.

The ideal candidate will have:

PhD (or equivalent experience) in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or a related field.

Algorithm design skills to tackle complex challenges in digital twin technologies.

Familiarity with modern data science and AI techniques, including the development and application of artificial intelligence, foundation models, and agentic AI systems.

Required skills

Design, implement, and evaluate novel graph representation learning methods for knowledge graph reasoning and completion, and develop end-to-end pipelines that integrate graph-based reasoning with large language models.

Hands-on experience in multi-modal learning, combining structured (graph) and unstructured (text, image, omics) data for patient-level representation learning.

Background in life sciences and/or biomedical domain, even minor, is highly encouraged.

Strong engineering skills in PyTorch/PyTorch Lightning and relevant ML libraries (PyTorch Geometric, DGL, etc.) for implementing custom architectures, paired with best practices in reproducible software development (Git workflows, testing, linting, documentation, CI/CD).

Familiarity with containerization and environment management tools (e.g., Docker, uv, Conda) and orchestration of large-scale ML experiments on cloud platforms.