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About the Role
The Computational Sciences Center of Excellence (CoE) aims to harness the power of data and Artificial Intelligence (AI) to assist scientists in drug discovery and development, accelerating R&D and delivering innovative medicines for patients worldwide. Frontier Research focuses on foundational machine learning research and developing new algorithmic frameworks, operating with a flat scientific structure and valuing open science.
Responsibilities
- 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 transcend off-the-shelf ML paradigms, creating robust, deployable architectures tailored to drug discovery.
- Partner and collaborate closely with cross-functional research teams across global innovation hubs.
- Build, experiment with, and scale unconventional neural network architectures using standard deep learning frameworks.
- Translate abstract biological challenges into rigorous computational models capable of bridging the gap between machine learning theory and clinical impact.
- Author and drive high-impact scientific publications for submission to top-tier machine learning and computational biology venues.
- Present research findings and represent the Frontier Research team at internal symposia and leading international scientific conferences.
- Actively engage in knowledge-sharing, code reviews, and technical discussions that elevate computational capabilities and foster an open, innovative team dynamic.
Requirements
- Currently pursuing a Ph.D. (or exceptional Master's degree) in Computer Science, Statistics, Applied Mathematics, Computational Biology, Physics, or a closely 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.
- Demonstrated track record of scientific contribution, evidenced by publications or accepted manuscripts at premier machine learning conferences or journals.
- Exposure to or strong interest in working with biological datasets and molecular systems.
- Outstanding collaborative and interpersonal skills, capable of communicating intricate technical ideas clearly across multi-disciplinary, distributed global teams.
- Driven by curiosity, unconventional problem-solving, and a commitment to core values of Integrity, Courage, and Passion.
Skills
- Machine learning algorithms
- Causal representation learning
- Out-of-distribution generalization
- Deep neural networks
- Modern deep learning frameworks (PyTorch strongly preferred)
- Scientific publications
- Computational models
- Generative modeling
Location
- Basel
- New York
- San Francisco
Work Type
- Full-time
Experience Level
- Ph.D. candidate or exceptional Master's degree holder
- Strong practical research experience
Education Level
- Ph.D. or Master's degree
About the Company
- A healthier future drives us to innovate.
- Together, more than 100’000 employees across the globe are dedicated to advance science, ensuring everyone has access to healthcare today and for generations to come.
- Our efforts result in more than 26 million people treated with our medicines and over 30 billion tests conducted using our Diagnostics products.
- We empower each other to explore new possibilities, foster creativity, and keep our ambitions high, so we can deliver life-changing healthcare solutions that make a global impact.
Equal Opportunity
- Roche is an Equal Opportunity Employer.