Postdoctoral Fellow – AI, Biomedical Imaging & Statistical Genetics

The fellow will pursue independent research while collaborating with leading scientists across Amgen deCODE genetics and the broader Amgen Research organization. The project brings together expertise in machine learning, image analysis, statistical genetics, multi-omics, and translational science, providing a unique environment for high-impact research at the interface of computational science and human biology.

Key Responsibilities include:

Conduct independent research at the intersection of biomedical image analysis, artificial intelligence, and statistical genetics.

Develop and validate state-of-the-art AI and machine learning methods for automated image segmentation, representation learning, and extraction of quantitative image-derived phenotypes (IDPs).

Analyze population-scale imaging, genomic, multi-omics, and clinical data to identify biological mechanisms underlying human anatomical and functional variation.

Perform genetic association and statistical analyses to characterize the genetic architecture of imaging-derived phenotypes and identify potential therapeutic targets.

Develop robust and reproducible computational workflows for large-scale biomedical imaging and genetic analyses.

Collaborate with multidisciplinary teams spanning AI, human genetics, bioinformatics, clinical science, and therapeutic discovery.

Present scientific findings at internal and external scientific forums and publish results in peer-reviewed journals.

This is a four-year fellowship within the Amgen Research & Development Postdoctoral Program, designed to provide advanced scientific training, mentorship, and career development opportunities for outstanding early-career researchers.

What We Expect of You

The Postdoctoral Fellow we seek is an innovative and highly motivated quantitative scientist with the following qualifications.

Basic Qualifications

Doctorate degree (PhD) completed prior to the start date from an accredited university in Engineering, Artificial Intelligence, Computer Science, Bioinformatics, Mathematics, Biostatistics, Biomedical Engineering, or a related quantitative discipline.

Preferred Qualifications

Strong record of scientific achievement demonstrated through peer-reviewed publications and scientific presentations.

Expertise in one or more of the following areas: artificial intelligence, machine learning, biomedical image or signal processing, statistical genetics, biostatistics, or computational genomics.

Experience analyzing large-scale biomedical imaging, human genetic, genomic, multi-omics, or other high-dimensional biomedical datasets is desirable.

Experience developing or applying machine learning or computational methods for large-scale data analysis.

Proficiency in Python, R, C/C++, or similar programming languages.

Experience with deep learning frameworks and modern image-analysis methods is desirable.

Experience working in Linux environments and with high-performance or GPU computing is desirable.

Demonstrated ability to develop innovative analytical approaches to complex scientific questions.

Strong written and verbal communication skills.

Ability to work effectively both independently and as part of a collaborative, multidisciplinary research environment.

What You Can Expect From Us

The Amgen Postdoctoral Program provides fellows with the opportunity to conduct innovative research in a world-class biotechnology environment while receiving mentorship from scientific leaders across multiple disciplines. Fellows are encouraged to publish their findings, present at leading scientific conferences, and develop the scientific and professional skills needed for successful careers in academia, biotechnology, or the pharmaceutical industry.

At Amgen deCODE genetics, you will join a highly collaborative scientific community with deep expertise in human genetics, population-scale biomedical data, artificial intelligence, and drug discovery. You will have access to large-scale imaging resources, linked genomic and multi-omics data, longitudinal clinical information, and advanced computational infrastructure for developing and applying cutting-edge analytical methods. The project includes access to GPU and CPU computing for large-scale model development and analysis.

.