Research Scientist (Machine Learning) - Virtual Patient Engine (VPE)

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.

Do you have experience in Python?, Do you have a Doctoral degree?, * 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., * 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.
• Ability to work independently and as part of an interdisciplinary team, and to drive research projects from conception to production.
• Strong problem-solving skills and scientific curiosity.
• Excellent communication, organizational, and interpersonal skills., * Experience with biomedical knowledge graphs (PrimeKG, DrugBank, STRING) or biological ontologies (GO, MONDO, etc.).
• Exposure to agentic AI frameworks (e.g., LangGraph or equivalents).
• Familiarity with causal discovery/inference, active learning, and inverse design.

Pulled from the full job description

• Flexible schedule, * A dynamic, collaborative research environment at the interface of ML and biomedical science.
• Close collaboration with our pharma partner (Sanofi) providing real-world biological problems and datasets.
• Flexible working hours and hybrid working within Germany.
• Access to a vast scientific network.
• Opportunities to publish in top academic journals and present at top academic and industry conferences.
• Mentorship and career development support, including conference attendance and networking opportunities.
• Training in how scientific teams take a high risk and high reward idea from development to early stage productization.
• International, diverse, and positive work atmosphere that fosters personal and professional growth.
• Team recognition events, complimentary Coursera courses, complimentary fresh fruit, soft drinks, and chocolate, etc.

The position is sponsored by Sanofi and is immediately available.

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.