Computational Analysis of Large Biomedical Datasets

Application deadline closed.

Job Description

The Stewart laboratory is recruiting a postdoctoral researcher to develop novel computational data analysis tools and apply them to large biomedical datasets. This position involves the integration of multiple data types, including but not limited to: RNA-seq (bulk, single-cell, spatial), ATAC-seq, and large text datasets such as PubMed or patents. The goal is to provide further supporting evidence for existing hypotheses and to generate new hypotheses, identifying new areas of exploration for wet lab collaborators in biomedicine. The specific focus of the project will depend on the candidate’s areas of interest and expertise and the needs of the lab and collaborators.

Successful candidates will join an experienced, diverse, and growing multidisciplinary group focused on the goal of understanding human biology. For more information, please see: Stewart Computational Group.

Candidate Requirements:
Candidates should have a Ph.D. in Computer Science, Bioinformatics, Computational Biology, or a related field, and proven research productivity as demonstrated by publications. It is expected that the candidate will have experience in one or more of the following areas: biomedical text mining, machine learning, natural language processing and large language models, RNA-seq or other omics analysis, or similar. The ability to build integrative models across multiple datatypes is highly desired. The ideal candidate will be a highly motivated, creative person with the ability to learn quickly and function both independently and within a team, and with excellent written and oral communication skills.

Qualified candidates interested in this opportunity are required to submit a cover letter and a curriculum vita via The cover letter should detail current and previous research, including projects/ideas they are interested in pursuing as a postdoctoral fellow in the Stewart Group, and provide names/address of three references.

How should applicants apply?
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