Postdoctoral Scholar-Pharmacology

JobID: 8792

Category: Research

JobSchedule: Full time

Posted Date: 2026-08-25T18:32:07+00:00

JobShift:

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THIS IS A GRANT-FUNDED POSITION FUNDED UNTIL OCTOBER 1, 2030

The Department of Pharmacology, Addiction Science, and Toxicology at the University of Tennessee Health Sciences is seeking a Postdoctoral Scholar to lead the computational analysis and artificial intelligence (AI) integration for a major NIH funded grant. The successful candidate will be responsible for extracting biological insights from large-scale, high-dimensional sequencing data using a combination of conventional bioinformatics and cutting-edge machine learning methodologies.

EDUCATION: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Neuroscience, or a related quantitative field.

EXPERIENCE: Experience in AI integration, extracting biological insights from large-scale high-dimensional sequencing data. Computational biology and AI preferred.

KNOWLEDGE, SKILLS, AND ABILITIES:

• Strong understanding of long-read sequencing technologies and multi-omics integration.
• Ability to work independently in a fast-paced, multi-disciplinary research environment.
• Excellent communication skills for collaborating with experimentalists and disseminating research outputs.
• Leads the analysis of foundational multi-omics datasets, including single-molecule long-read DNA methylation (CpG), direct RNA sequencing, and single-nucleus RNA-seq (snRNA-seq) generated across diverse rat strains and brain regions.
• Adapts and fine-tunes existing deep learning models (e.g., AlphaGenome, DeepSEA, DNA Hyena, scGPT) to improve variant effect prediction and automated cell-type annotation specifically for rat genomic data.
• Develops and implements a Retrieval-Augmented Generation (RAG) framework utilizing Large Language Models (LLMs) to synthesize information from biomedical literature and generate novel, testable hypotheses regarding Substance Use Disorder (SUD) mechanisms.
• Utilizes advanced statistical frameworks (e.g., Multi-Omics Factor Analysis) to integrate genomic, epigenomic, transcriptomic, and proteomic data.
• Drafts high-impact manuscripts for peer-reviewed journals and present research findings and resources at national and international conferences.
• Oversees the utilization of high-performance computational resources, including dedicated GPU workstations for LLM evaluation and testing.
• Performs other duties as assigned.
• Leads the analysis of foundational multi-omics datasets, including single-molecule long-read DNA methylation (CpG), direct RNA sequencing, and single-nucleus RNA-seq (snRNA-seq) generated across diverse rat strains and brain regions.
• Adapts and fine-tunes existing deep learning models (e.g., AlphaGenome, DeepSEA, DNA Hyena, scGPT) to improve variant effect prediction and automated cell-type annotation specifically for rat genomic data.
• Develops and implements a Retrieval-Augmented Generation (RAG) framework utilizing Large Language Models (LLMs) to synthesize information from biomedical literature and generate novel, testable hypotheses regarding Substance Use Disorder (SUD) mechanisms.
• Utilizes advanced statistical frameworks (e.g., Multi-Omics Factor Analysis) to integrate genomic, epigenomic, transcriptomic, and proteomic data.
• Drafts high-impact manuscripts for peer-reviewed journals and present research findings and resources at national and international conferences.
• Oversees the utilization of high-performance computational resources, including dedicated GPU workstations for LLM evaluation and testing.
• Performs other duties as assigned.