PhD

The ideal candidate brings a sophisticated background in the development and rigorous testing of foundational machine learning models, specifically demonstrating the ability to evaluate model performance and safety in high-stakes medical contexts. Success in this role requires extensive experience navigating high-performance computing environments and GPU clusters to manage the intense computational demands of large-scale model training. Furthermore, the fellow must possess a proven aptitude for large-scale data curation, including the processing of heterogeneous datasets like whole-slide imaging and next-generation sequencing, ensuring that data pipelines are both efficient and ethically compliant for clinical research.

N/A

Lifting, pushing or pulling of objects up to 25 pounds, sitting and/or standing for long periods of time.

Monday Friday 8am-5pm

This postdoctoral position focuses on the cutting edge of digital medicine, primarily tasking the fellow with the design and implementation of foundational models across the distinct yet intersecting domains of computer vision, genomics, and natural language. The central mission involves synthesizing these individual modalities into sophisticated generative multi-modal models that can navigate the complexities of pathology informatics and broader clinical environments. By bridging these data silos, the researcher will develop AI systems capable of cross-domain reasoning, such as generating synthetic pathology insights from genomic signatures or interpreting high-resolution tissue images through the lens of unstructured clinical narratives to advance diagnostic precision.

PhD

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Any candidate offered a position may be required to pass pre-employment screenings as mandated by University of Kentucky Human Resources. These screenings may include a national background check and/or drug screen.