The requirements

• Currently pursuing a Ph.D. (or exceptional Master's degree) in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics, or a related technical field, with strong practical research experience.
• Solid grounding in machine learning theory and principles, with practical familiarity or strong interest in causal representation learning and/or out-of-distribution generalization.
• Hands-on expertise in building, training, and troubleshooting deep neural networks, with proficiency in modern deep learning frameworks (PyTorch strongly preferred).
• Demonstrated track record of scientific contributions, with publications or accepted manuscripts at premier machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, AISTATS, UAI, CVPR, ACL).
• Outstanding collaborative and interpersonal skills, capable of clear communication across multi-disciplinary, distributed global teams.
• Driven by curiosity, unconventional problem-solving, with commitment to Integrity, Courage, and Passion.

Nice to haves

• Exposure to or strong interest in working with biological datasets and molecular systems.
• Prior direct biological experience is an asset but not mandatory.

What you'll be doing

• Design, implement, and benchmark novel machine learning algorithms grounded in causal representation learning and out-of-distribution generalization to address complex biological problems.
• Frame foundational research questions that go beyond off-the-shelf ML paradigms, creating robust and deployable architectures tailored to drug discovery.
• Collaborate closely with cross-functional research teams across global innovation hubs in Basel, New York, and San Francisco.
• Build, experiment with, and scale unconventional neural network architectures using standard deep learning frameworks.
• Translate abstract biological challenges into rigorous computational models bridging ML theory and clinical impact.
• Author and lead high-impact scientific publications for top-tier ML and computational biology venues.
• Present research findings at internal symposia and leading international scientific conferences.
• Engage in knowledge-sharing, code reviews, and technical discussions to elevate computational capabilities and foster an open, innovative team culture.