Postdoctoral Fellow – AI, Computational Biology, & Systems Biology

The Pennsylvania State University • University Park, FL

5d • 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.

Requirements

• 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.
• 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.
• 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.
• 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

• 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.
• Experience with long-read transcriptomics/PacBio HiFi, single-cell transcriptomics, gene regulatory network inference, and working with HPC environments and reproducible computational workflows.

Responsibilities

• Contribute to projects involving multi-omics data integration across genomics, transcriptomics, proteomics, epigenomics, and metabolomics.
• Contribute to projects involving long-read RNA sequencing and rRNA isoform discovery using PacBio HiFi and other long-read platforms.
• Contribute to projects involving single-cell RNA-seq analysis and cell-type-specific molecular profiling.
• Contribute to projects involving gene regulatory network inference using approaches such as GENIE3, SCENIC, and related network-based methods.
• Develop computational approaches for therapeutic target discovery and prioritization.
• Integrate heterogeneous datasets to generate and test mechanistic biological hypotheses.
• 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