Senior Image Data Scientist-Center for Bioimage Informatics

The successful candidate will support image data management, maintain analysis software and repositories, evaluate new methods and tools, and provide technical consultation and training to research teams. This role also serves as a project lead to coordinate image-analysis efforts, align related projects, and contribute to shared CBI infrastructure that supports routine and advanced bioimage analysis.

Applicant Statement:

Applicants are encouraged to include a brief cover letter or supplemental statement with a link to representative work, such as a public repository, napari plugin, published pipeline, or comparable artifact. The statement should briefly describe one end-to-end image-analysis project or software tool the applicant has owned in production, including the biological or scientific question, data scale, methods or model used, pipeline design, validation approach, user adoption, and resulting impact.

Preferred Qualifications:

• PhD in a relevant quantitative, computational, biomedical, or scientific field.

• Experience developing, validating, and maintaining image-analysis pipelines for biological or medical imaging data.

• Strong programming skills in Python and experience with modern computer vision, machine learning, deep learning, or statistical image-analysis methods.

• Experience with emerging AI methods relevant to bioimage analysis, including vision transformers, foundation models, generative AI, large language models (LLMs), or agentic AI tools.

• Experience with scalable and reproducible scientific workflows (e.g., Snakemake, Nextflow), containers, version control, and HPC or cloud environments.

• Demonstrated experience using modern bioimaging tools, data standards, and open-source ecosystems such as napari, ImageJ/Fiji, Cellpose, SAM-family and related foundation segmentation models, QuPath, BioFormats, OME-Zarr/NGFF, or similar platforms.

• Experience with large image data volumes, image data management, quality control, benchmarking, annotation strategies, and method evaluation.

• Demonstrated ability to collaborate with research teams, communicate technical results clearly, write user-facing documentation, and provide training through courses, workshops, seminars, or similar formats.

• Evidence of technical leadership, such as leading multi-lab projects, contributing to shared infrastructure, publishing methods, or contributing to open-source scientific software.