美国宾夕法尼亚大学2025年博士后招聘（计算生物学AI/ML和单细胞生物学）
美国宾夕法尼亚大学2025年博士后招聘（计算生物学AI/ML和单细胞生物学）
Postdoctoral Fellow – Computational biology AI/ML and Single-Cell Biology/ University of Pennsylvania – Susztak Lab
University of Pennsylvania
Location: Philadelphia, PA
Job Number: 7312882
Posting Date: Newly posted
Application Deadline: Open Until Filled
Job Description
The Susztak Lab at the University of Pennsylvania invites applications for a Postdoctoral Fellow position at the intersection of computational biology, artificial intelligence, and single-cell omics. Our lab is pioneering the development of multi-species single-cell atlases and building foundation models for the kidney and beyond.
We seek a highly motivated and creative individual with expertise in machine learning, deep learning, or computational genomics to join our multidisciplinary team. The successful candidate will develop and apply novel algorithms, including transformer-based foundation models, to interpret large-scale single-cell and spatial transcriptomics datasets in both health and disease.
What we offer:
Access to one of the largest, best-annotated human and rodent kidney single-cell atlases
Integration of genomic, epigenomic, spatial, and clinical datasets
A collaborative environment with leading experts in nephrology, systems biology, and AI
Opportunities to publish in high-impact journals and present at international conferences
Qualifications:
PhD in computer science, bioinformatics, computational biology, or related fields
Strong programming skills (Python, PyTorch/TensorFlow, R)
Experience with AI/ML, large-scale omics, or foundation model development
Prior work in single-cell or spatial transcriptomics is a plus but not required
Location: Philadelphia, Pennsylvania, USA (in-person position)
Start date: Flexible; positions available immediately
To apply, please send your CV, a brief cover letter, and contact information for 2–3 references to ksusztak@pennmedicine.upenn.edu. Applications will be reviewed on a rolling basis.
Join us to push the boundaries of AI in biology and help build the next generation of diagnostics and therapeutics through computational innovation.
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