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

Festanstellung, * Qualifikationslevel B Ausübungsformen

Gewünschte Fähigkeiten & Kenntnisse

Machine Learning Network Edge Cloud CAN Forecasting Origin MS Access Mobile App Docker Hybrid Innovation Active Directory GIT Digitale Zwillingstechnologie, 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.

• PhD (or equivalent experience) in Computer Science, Machine Learning, Applied Mathematics, Computational Biology, or a related field.
• You will leverage your advanced algorithm design skills to tackle complex challenges in digital twin technologies.

• You will be part of a dynamic team driving discovery through cutting-edge data science, including the development and application of artificial intelligence, foundation models, and agentic AI systems. Required skills

• Background in algorithm development for multi-variate time-series data, including generative modeling, spatial-temporal and graph-based approaches (e.g., Ordinary Differential Equations (ODE)- or Neural Differential Equations (NDE)-based models), and time-series foundation models.
• Hands-on experience applying these methods to heterogeneous data integration and forecasting using biomedical knowledge graphs.
• Background in life sciences and/or mathematical modeling of biological systems, even minor, is highly encouraged.
• Strong engineering skills in PyTorch/PyTorch Lightning 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 as part of an interdisciplinary team but also independently.
• Strong problem-solving skills and scientific curiosity.

• Excellent communication, organizational, and interpersonal skills. Additional skills, good to have

• Background in biology, computational biology or mathematical modeling of biological systems.
• Exposure to agentic AI frameworks (e.g., LangGraph or equivalents).
• Experience incorporating inductive biases and physics/biology-inspired constraints into models.
• Familiarity with causal discovery/inference, active learning, and inverse design.

• This position is initially limited until March 31, 2027, with the option of extension for an additional year subject to performance evaluation.
• Flexible working hours and hybrid working location.
• Access to a vast scientific network. The team will be working closely with our industry partner Sanofi, including various high profile academic partners, and industry-academia consortiums.
• Opportunities to publish in top academic journals and present at top academic and industry conferences.
• 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.
• Job ticket, complimentary fresh fruit, soft drinks and chocolate, team recognition events, complimentary Coursera courses, etc. The position is sponsored by Sanofi and is immediately available.

BioMed X is an independent research institute located on the campus of the University of Heidelberg in Germany, New Haven in Connecticut, XSeed Labs in Ridgefield, Connecticut, and a world-wide network of partner locations. Together with our partners, we identify big biomedical research challenges and provide creative solutions by combining global crowdsourcing with local incubation of the world’s brightest early-career research talents. Each of the highly diverse research teams at BioMed X have access to state-of-the-art research infrastructure and is continuously guided by experienced mentors from academia and industry. At BioMed X, we combine the best of two worlds - academia and industry - and enable breakthrough innovation by making biomedical research more efficient, more agile, and more fun.