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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