Indiana University Bloomington
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Postdoctoral Fellow in Biology, Spatial Population Genetics and Machine Learning Indiana University Bloomington in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Initial appointment is for one year with potential reappointment for additional years contingent on satisfactory performance and funding availability. Salary will be commensurate with experience and full benefits are included.
Country
United States
University
Indiana University Bloomington

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About this position
Postdoctoral Fellow, Biology Smith Lab at Indiana University Bloomington invites applications for a postdoctoral research position in spatial population genetics, simulation-based inference, machine learning, deep learning, bioinformatics, and genomics.
The Smith Lab studies how evolutionary and ecological processes shape genetic variation using publicly available and simulated genomic data. The postdoc will build computational pipelines for simulation experiments and genomic sequencing analysis, design and oversee research projects focused on methods development, and publish and present findings at conferences.
Required background: a Ph.D. in population genetics, computational biology, bioinformatics, computer science, or a related field, plus demonstrated experience leading computational research projects. Preferred experience includes Python, population genetics, reproducible analysis pipelines, and deep learning libraries such as PyTorch or TensorFlow.
Funding: the appointment is initially for one year, with possible renewal for additional years depending on performance and funding availability. Salary is commensurate with experience and full benefits are included.
How to apply: submit a cover letter, CV, and two references through the application portal. Applications are reviewed on a rolling basis until the position is filled. Best consideration date: 2026-10-10. Expected start date: 2026-11-01.
Funding details
Initial appointment is for one year with potential reappointment for additional years contingent on satisfactory performance and funding availability. Salary will be commensurate with experience and full benefits are included.
What's required
Ph.D. in population genetics, computational biology, bioinformatics, computer science, or a related field is required. Demonstrated experience leading computational research projects is required. Preferred skills include scientific computing, reproducible analysis pipelines, strong time management and organization, teamwork, excellent written and oral communication, dependability, initiative, professionalism, Python, population genetics experience, and experience with deep learning libraries such as PyTorch or TensorFlow.
How to apply
Submit a cover letter, current CV, and names/contact information for two references through the application link. Questions about the position can be sent to Chris Smith by email.
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