Postdoctoral Fellow - AI, Computational Biology, & Systems Biology
 The Pennsylvania State University | |
remote work
| |
 United States, Pennsylvania, University Park | |
| 201 Old Main (Show on map) | |
 Sep 28, 2026 | |
APPLICATION INSTRUCTIONS:
Approval of remote and hybrid work is not guaranteed regardless of work location.For additional information on remote work at Penn State, seeNotice to Out of State Applicants. This is a term position; length of the term will be discussed during the interview process. Continuation past the termlengthdiscussed willbebasedonuniversityneed,performance,and/oravailabilityoffunding.POSITION SPECIFICS We are seeking a highly motivated Postdoctoral Fellow in AI, Computational Biology, and Systems Biology, to join an interdisciplinary research program focused on understanding the molecular mechanisms underlying complex biological mechanisms using the integration of large-scale multi-omic datasets. The successful candidate will develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes to identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets. The position provides an opportunity to work at the intersection of physiology, AI, computational biology, and systems biology, with access to increasingly large and diverse multi-omic datasets. A major focus of the position will be the development of computational frameworks to decipher organ-cross talk by connecting molecular dataset (multi-omics) to cellular phenotypes, tissue-specific regulatory programs, and physiological responses to exercise. Candidates with expertise in multi-omics integration, single-cell and long-read transcriptomics are particularly encouraged to apply. Research Areas The postdoctoral fellow will have opportunities to contribute to projects involving: Multi-omics data integration across genomics, transcriptomics, proteomics, epigenomics, and metabolomics, long-read RNA sequencing and rRNA isoform discovery using PacBio HiFi and other long-read platforms, single-cell RNA-seq analysis and cell-type-specific molecular profiling, gene regulatory network inference using approaches such as GENIE3, SCENIC, and related network-based methods, develop computational approaches for therapeutic target discovery and prioritization, and integration of heterogeneous datasets to generate and test mechanistic biological hypotheses. Candidate Profile We are seeking candidates with a Ph.D., M.D., or equivalent doctoral degree in bioinformatics, computational biology, systems biology, genomics, genetics, biostatistics, biomedical engineering, molecular biology, or a related discipline with documented experience using the appropriate methodology described above including but not limited to long-read RNA-seq analysis (PacBio HiFi sequencing), transcript and isoform discovery, alternative splicing and isoform characterization, genome/transcriptome alignment and annotation, single-cell and gene regulatory network discovery using scRNAseq, and integration of single-cell data with bulk and multi-omic datasets Desired computational skills: Strong programming experience in Python and/or R is expected. Experience with Linux/HPC environments, workflow development, statistical modeling, machine learning, and reproducible computational pipelines is highly desirable. Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases would be advantageous. Candidates with experience developing new computational methods rather than exclusively applying existing pipelines are particularly encouraged to apply. Research environment The fellow will work in a highly interdisciplinary environment involving computational biologists, molecular biologists, physiologists, and data scientists. The position provides opportunities to develop independent research directions while contributing to collaborative projects involving large-scale human and experimental datasets. The candidate will lead and co-lead publications, present research at national and international conferences, develop grant proposals and fellowship applications, collaborate with experimental investigators to generate and test computationally derived hypotheses, and develop expertise in emerging multi-omic and AI-enabled approaches to biomedical research Required qualifications Ph.D., M.D., or equivalent doctoral degree in a relevant field Demonstrated research experience in computational biology, bioinformatics, genomics, or a related field Strong programming skills in R and/or Python Experience analyzing high-throughput biological datasets Strong quantitative, analytical, and problem-solving skills Excellent written and oral communication skills Preferred qualifications: Experience with long-read transcriptomics/PacBio HiFi, single-cell transcriptomics, gene regulatory network inference, and working with HPC environments and reproducible computational workflows How to Apply Applicants should submit: 1. A curriculum vitae 2. A brief statement describing research experience and interests 3. A description of relevant computational and bioinformatics expertise 4. Contact information for three professional references Applications will be reviewed on a rolling basis until the position is filled. BACKGROUND CHECKS/CLEARANCES Employment with the University will require successful completion of background check(s) in accordance with University policies.BENEFITS Penn State provides a competitive benefits package for full-time employees designed to support both personal and professional well-being.For more detailed information, please visit ourBenefits Page. (Note: For Postdoctoral benefits, please see our Postdoctoral Benefits page.) CAMPUS SECURITY CRIME STATISTICS Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here. EEO IS THE LAW Penn State is an equal opportunity employer and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473. Federal Contractors Labor Law Poster PA State Labor Law Poster Penn State Policies Copyright Information Hotlines | |