Postdoctoral Fellow – AI, Computational Biology, & Systems Biology

Penn State University • University Park, IL

6d • Hybrid

About The Position

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.

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.

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.

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.

Requirements

• 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

Nice To Haves

• Expertise in multi-omics integration, single-cell and long-read transcriptomics
• Experience with long-read transcriptomics/PacBio HiFi
• Experience with single-cell transcriptomics
• Experience with gene regulatory network inference
• Experience working with HPC environments
• Experience with reproducible computational workflows
• Experience with Linux/HPC environments
• Experience with workflow development
• Experience with statistical modeling
• Experience with machine learning
• Experience with tools and resources such as Scanpy, Seurat, GENIE3, SCENIC, DESeq2, STAR, minimap2, Salmon, kallisto, Bioconductor, Ensembl, GTEx, and protein-interaction databases
• Experience developing new computational methods rather than exclusively applying existing pipelines

Responsibilities

• Develop and apply computational and statistical approaches to integrate genomic, transcriptomic, epigenomic, proteomic, and physiological phenotypes.
• Identify molecular mechanisms, regulatory networks, disease-associated pathways, and potential therapeutic targets.
• Develop 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.
• Contribute to projects involving multi-omics data integration, long-read RNA sequencing, single-cell RNA-seq analysis, gene regulatory network inference, and therapeutic target discovery.
• 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.
• Develop expertise in emerging multi-omic and AI-enabled approaches to biomedical research.

Benefits

• Competitive benefits package for full-time employees designed to support both personal and professional well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree