Postdoctoral Fellow in Machine Learning and Computational Biology, University of Memphis

Job:
Postdoctoral Fellow in Machine Learning and Computational Biology, The University of Memphis

A postdoctoral fellow position is available for an outstanding candidate interested in developing and applying state-of-the-art machine learning methods to multi-omics data to identify biomarkers for post-traumatic stress disorder (PTSD). The successful applicant will join the Systems Biology of PTSD Biomarkers Consortium (SBPBC), a long-standing collaboration between the University of Memphis, New York University (NYU), Harvard University, Brown University, the University of California San Francisco (UCSF), the Institute for Systems Biology (ISB), and Walter Reed Army Institute of Research (WRAIR).

Over the last 15 years, the SBPBC has measured >1 million blood-based molecular markers in >13,000 military service members and civilians with and without PTSD. Members of the Daigle Lab have successfully applied machine learning methods to these markers to enable accurate PTSD diagnosis, prognosis, and clinical subtyping. The postdoctoral fellow will lead efforts to develop novel machine learning models for integrating omics datasets (e.g., genomic, transcriptomic, epigenomic, proteomic, metabolomic) with relevant molecular pathways to further improve these capabilities.

The position offers outstanding opportunities for collaboration with members of the SBPBC and access to comprehensive high-dimensional molecular datasets for the development and application of cutting‑edge machine learning methods.

We seek highly motivated candidates with the ability to work both independently and in a collaborative environment. The position offers a competitive salary plus benefits for an initial appointment of 12 months, with the potential for extension contingent upon continued funding and satisfactory performance. Screening of applicants will begin June 5, 2025, but the position is open until filled. If interested, please  for more information and to apply.