Research Associate (Postdoc)

The Nirschl laboratory develops AI methods for human neurodegenerative disease. Projects combine whole-slide pathology, clinical and imaging records, molecular measurements and spatial omics to study tissue phenotypes and validate biomarkers in Alzheimer’s and Lewy body diseases. Responsibilities include model development and evaluation, reproducible analysis pipelines, collaboration, publications and mentoring. This full-time appointment follows applicable NIH postdoctoral salary levels and experience; the start is flexible.

Email jjnirschl@wisc.edu with subject “Nirschl Lab Bio-CV/ML Postdoc”: one PDF containing a cover letter of at most two pages, CV, a representative lead paper or preprint, portfolio links where applicable, and contact details for three references.

The Nirschl Lab at the University of Wisconsin–Madison is recruiting a postdoctoral researcher in biomedical computer vision and machine learning to develop computational approaches for studying human neurodegenerative disease.

Our research combines artificial intelligence, computational pathology, neuropathology, spatial omics, and deeply characterized human tissue cohorts to investigate Alzheimer’s disease, Lewy body disease, and other age-related neurodegenerative diseases.

The successful candidate will have opportunities to develop new computational methods for digital pathology and multimodal biomedical data, including projects focused on:
- Computer vision and machine learning for whole-slide pathology images
- Computational pathology and quantitative tissue phenotyping
- Multimodal integration of pathology, clinical, imaging, and molecular data
- Antemortem–postmortem studies for biomarker discovery and validation
- Spatial transcriptomics and other spatial-omics technologies
- Cellular and tissue mechanisms underlying selective vulnerability and disease heterogeneity in Alzheimer’s disease, Lewy body disease, and related neurodegenerative disorders

The position is particularly well suited to a computational scientist interested in developing innovative AI methods while working closely with neuropathologists, neuroscientists, biomedical researchers, and collaborators across disciplines.

Ideal candidates will have:
- A PhD in computer science, electrical or computer engineering, biomedical engineering, computational biology, bioinformatics, data science, applied mathematics, statistics, neuroscience, or a closely related quantitative field, conferred by the start date
- Strong experience in computer vision, machine learning, deep learning, or biomedical image analysis
- Proficiency in Python and modern machine-learning workflows and frameworks
- Experience designing, training, evaluating, and interpreting machine-learning models
- Strong quantitative, analytical, and problem-solving skills
- Experience working with large or complex biomedical datasets
- Demonstrated ability to conduct rigorous, reproducible computational research
- Strong scientific communication skills and a record of scholarly productivity
- Ability to work independently while collaborating effectively within multidisciplinary research teams

Experience in one or more of the following areas is desirable but not required:
- Digital pathology or whole-slide image analysis
- Computational pathology
- Multiple-instance learning, weakly supervised learning, self-supervised learning, or foundation models
- Multimodal learning and data integration
- Spatial transcriptomics, spatial proteomics, or other spatial-omics data
- Computational biology or biomedical informatics
- Human neuropathology or neurodegenerative disease research
- Alzheimer’s disease, Lewy body disease, tauopathies, or related disorders
- High-performance or distributed computing
- Open-source scientific software development

Prior pathology or neurodegeneration experience is not required. We particularly encourage applications from strong computer vision and machine-learning researchers interested in applying and extending their methods to important problems in human disease.