Postdoctoral Scholar-Pharmacology

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.

Responsibilities

1. 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.
2. 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.
3. 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.
4. Utilizes advanced statistical frameworks (e.g., Multi-Omics Factor Analysis) to integrate genomic, epigenomic, transcriptomic, and proteomic data.
5. Drafts high-impact manuscripts for peer-reviewed journals and present research findings and resources at national and international conferences.
6. Oversees the utilization of high-performance computational resources, including dedicated GPU workstations for LLM evaluation and testing.
7. Performs other duties as assigned. 

Qualifications

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.