Postdoctoral Scholar-Pharmacology
University of Tennessee at Martin
Salary undisclosed
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Description
- 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.
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.
About this role
At the University of Tennessee at Martin, this postdoctoral position centers on applying cutting-edge computational and AI methods to understand the genetic and molecular basis of Substance Use Disorder. You'll lead the analysis of complex multi-omics datasets—spanning DNA methylation, RNA sequencing, and single-nucleus transcriptomics—collected across different rat strains and brain regions. The role requires adapting existing deep learning models to improve prediction accuracy for rat genomic data and building a novel framework that combines Large Language Models with biomedical literature to generate and test new hypotheses about SUD mechanisms.
The work is highly technical and computationally intensive. You'll integrate multiple data types using advanced statistical methods, manage GPU-intensive computational resources, and develop the analytical pipelines and models that drive the research forward. Success depends on strong expertise in long-read sequencing, multi-omics integration, and AI—particularly experience extracting biological insights from large-scale, high-dimensional data. You should be comfortable working independently while collaborating across a multidisciplinary team of experimentalists and computational researchers.
This role is best suited for someone with a Ph.D. in bioinformatics, computational biology, computer science, neuroscience, or a closely related quantitative field, and a demonstrated track record integrating AI into biological research. You'll be expected to author high-impact manuscripts and present findings at major conferences, so strong communication skills and the ability to translate complex computational work for diverse audiences are essential.
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