Postdoctoral Fellow in Machine Learning and Computational Biology

The University of Memphis, United States

Updated: 5 days ago
Location: Memphis, TENNESSEE
Deadline: ;

Posting Information


Posting Number FAE1908
Advertised Title Postdoctoral Fellow in Machine Learning and Computational Biology
Campus Location Main Campus (Memphis, TN)
Position Number 023825
Category Full-Time Faculty
Department Biology
Position Summary
The Daigle Lab of Bioinformatics and Systems Biology at the University of Memphis (UofM) is recruiting a postdoctoral fellow to develop and apply 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 UofM, 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. They will participate in the publication and presentation of project findings as well as assisting with funding proposals. They will also contribute to the training and mentoring of undergraduate and graduate trainees. 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.
Applications must be submitted online at https://workforum.memphis.edu/ and include a cover letter, CV, unofficial transcripts, two representative publications showcasing your writing and analytical skills, and contact information (not letters) for at least three professional references. Applicants can learn more about the Departments of Biological Sciences, Computer Science (both affiliated with the Daigle Lab), and Dr. Daigle at the following links: https://www.memphis.edu/biology/ , https://www.memphis.edu/cs/ , and https://daiglelab.org . Informal inquiries can be sent to bjdaigle[at]memphis.edu.
Hiring range: Commensurate with experience
Minimum Position Qualifications
· Ph.D. in bioinformatics/computational biology, genetics/genomics, computer science, data science, or similar
· Strong publication record in peer-reviewed conferences and/or journals
· Experience applying machine learning methods (especially deep neural network approaches) to genome-scale datasets
· Extensive programming experience in R and/or Python (ideally both)
· Strong writing, communication, and interpersonal skills, including a proven ability to work both independently and as part of a team
Special Conditions
This position is available for sponsorship consideration.
Posting Date 05/05/2025
Closing Date
Open Until Screening Begins Yes
Hiring Range Commensurate with experience
Full-Time/Part-Time
Special Instructions to Applicants
Instructions to Applicant regarding references
Contact information (not letters) for at least three professional references
Is this posting for UofM employees only? No
Benefits of Employment (Applies to full-time, non-police employees only)




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