Post Doctoral Fellowship - Applied Data Science
Core
Develop AI-powered data analysis tools and models to advance hypothesis-driven research on addiction, specifically focusing on gut microbiome-host interactions in cocaine use disorder and temporal architectures of opioid withdrawal.
Role type
Postdoctoral Researcher (Applied Data Science & AI)
Builds
Explainable graph neural network models, multimodal machine learning methods, and AI-guided experimental design tools for life sciences.
Domain
Life Sciences / Addiction Research / Artificial Intelligence
Deliverable
production ML models
Required skills
Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision (CV), Graph Neural Networks (GNN), Cross-species data translation, Multi-omics analysis
Preferred skills
Computational biology, Bioinformatics, Genetics, Neuroscience, Systems genetics, Microbiome research
Technologies
Graph Neural Networks, Multi-omics platforms, NIH-funded study datasets
Responsibilities
Lead development of explainable GNN models integrating microbiome, genetic, and behavioral data; Identify conserved biological pathways across mice and humans; Develop multimodal ML models to distinguish spontaneous versus precipitated opioid withdrawal; Collaborate with experimental researchers on AI-guided experimental design.
Seniority
Postdoctoral Fellow (Research)
