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.
• 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.

Please refer the Job description for details