Amgen Science Fellow - Computational Biology, AI, and Advanced Computing

Role Summary

Join Amgen's mission to serve patients as an Amgen Science Fellow. The program invites an exceptional early-career scientist to establish an independent research program at the intersection of drug discovery, computational biology, AI, and emerging computing paradigms, including quantum computing. The appointment is up to four years and focuses on developing and applying advanced computational methods to foundational questions in biology, chemistry, and therapeutic discovery. Fellows publish breakthrough findings and contribute to Amgen's research culture without focusing on pipeline programs.

Responsibilities

• Establish an independent research program at the intersection of drug discovery, computational biology, AI, and emerging computing paradigms including quantum computing.
• Develop and apply advanced computational methods to address foundational challenges in biology, chemistry, and therapeutic discovery, with emphasis on machine learning for biological inference and molecular design, large-scale computational and systems biology, hybrid AI–physics–based modeling, and exploratory quantum and quantum-inspired approaches.
• Define a forward-looking research agenda that advances computational science as a core driver of next-generation biopharmaceutical R&D.
• Dedicate time to developing a robust research project aimed at breakthrough discoveries and publication, with the opportunity to initiate and manage an independent project under technical direction; Fellows are not expected to work on pipeline programs.
• Appointment duration: up to four years.

Qualifications

• Required: Ph.D. in a computational or life sciences discipline with research experience in computational biology, AI, and advanced computing.
• Preferred: Demonstrated record of first-author or corresponding-author publications in high-impact journals at the intersection of computation and life sciences.
• Preferred: Evidence of independent scientific vision, such as leading a distinct research direction separate from PhD or advisor-driven programs.
• Preferred: Experience framing and pursuing long-horizon, high-risk scientific questions rather than incremental method development.
• Preferred: Advanced proficiency in modern machine learning architectures (e.g., graph neural networks, foundation models, generative models) applied to biological or chemical data.
• Preferred: Strong programming skills with production-quality code or open-source contributions.
• Preferred: Experience working with large-scale, multimodal biological datasets (omics, imaging, molecular, clinical, or simulation-derived).
• Preferred: Familiarity with high-performance computing environments, cloud-based workflows, or scalable model training.
• Preferred: Proven ability to integrate data-driven AI approaches with mechanistic, physics-based, or systems-level models.
• Preferred: Background in at least one core domain relevant to therapeutic discovery (e.g., structural biology, chemistry, systems biology, pharmacology), beyond purely computational training.
• Preferred: Prior exposure to quantum computing, quantum-inspired algorithms, or advanced optimization methods, with demonstrated curiosity about near-term scientific impact.
• Preferred: Willingness to prototype and critically evaluate immature or speculative technologies in a rigorous scientific context.
• Preferred: Track record of scientific leadership, including mentoring students, coordinating collaborations, or driving cross-disciplinary projects.
• Preferred: Strong written and verbal communication skills, with experience presenting complex computational ideas to experimental or translational audiences.

Education

• Ph.D. in a computational or life sciences discipline (computational biology, bioinformatics, computational chemistry, systems biology, pharmacology, or related field).