Senior Computational Research Scientist - Department of Imaging Sciences

The Manor Laboratory seeks an experienced computational imaging professional to develop and apply artificial intelligence, computer vision, and quantitative image analysis to challenging questions in cell biology and biomedical research. Working closely with Dr. Uri Manor, experimental scientists, and collaborators, this individual will provide technical leadership across the laboratory's computational imaging portfolio and translate advanced imaging data into validated biological measurements and broadly usable research tools.

The position spans fluorescence, label-free, live-cell, three- and four-dimensional (3D/4D), and electron microscopy. Applications include sensory-organ structure and pathology, cellular and organelle dynamics, and dense reconstruction of biological tissue. The role combines independent algorithm development, end-to-end scientific software engineering, collaborative research, and technical mentorship; it is not restricted to one disease, imaging modality, or biological system.

Partner with the principal investigator to prioritize and execute computer-vision and quantitative image-analysis projects across the laboratory. Translate biological questions into computational objectives, technical plans, and measurable deliverables. Establish reusable architectures, evaluation standards, and best practices that support consistent, reproducible analysis across imaging modalities and research programs.

Design, implement, and benchmark methods for segmentation, classification, detection, tracking, image reconstruction and restoration, denoising, virtual staining, resolution enhancement, and quantitative phenotyping. Apply appropriate approaches, including convolutional and recurrent neural networks, Transformers, multimodal learning, generative models, and vision-language models. Evaluate performance on independent data, assess failure modes and generalizability, and verify that image transformations preserve biologically meaningful information.

Develop quantitative analyses of multichannel 3D sensory-organ images and time-resolved cellular and organelle dynamics. Advance dense 3D segmentation and reconstruction of serial-section electron-microscopy data, including approaches that learn from sparse two-dimensional annotations. Work with experimental collaborators to connect computational outputs to interpretable measurements, rigorous biological conclusions, publications, and reusable research resources.

Lead end-to-end workflows spanning annotation strategy, dataset curation, model training, validation, deployment, and maintenance. Develop documented, tested, version-controlled software, including interactive desktop plugins and web-based analysis tools where appropriate. Optimize workflows for large, multidimensional datasets and GPU or parallel computing. Expand annotated resources, support generalizable models, and collaborate with institutional and external partners to make tools accessible, maintainable, and useful to researchers.

Provide technical guidance and mentorship to students, postdoctoral fellows, staff, and collaborators developing or applying AI tools. Advise on experimental design, data preparation, annotation, model selection, code development, validation, interpretation, and reproducibility. Develop training materials, lead hands-on instruction, and support adoption of shared tools. Contribute to manuscripts, grant applications, scientific presentations, and collaborative methods development while fostering a supportive, interdisciplinary research environment.

Minimum Education Requirements:

• Bachelor's degree in Bioinformatics, Molecular Biology, Biochemistry, Computer Science, or related field.
• Master's degree or PhD preferred.

Minimum Experience Requirements:

• Bachelor's degree and 7+ years of relevant experience.
• Exception: Master's degree and 5+ years of relevant experience (OR) PhD with 2+ years of relevant experience.
• Rough criteria for this position based on publication output: 1-2 first author papers IF > 10 (or equivalent contribution to other research outputs).
• Prior experience in computational research techniques and processes.
• Proven performance in earlier role/comparable role.

• A Ph.D. in computer science, computer engineering, electrical engineering, biomedical engineering, applied mathematics, or a closely related quantitative field.
• Demonstrated independent development and validation of machine-learning or computer-vision methods for biomedical images, supported by publications, deployed software, or comparable research outputs.
• Strong Python programming and experience with a major deep-learning framework such as PyTorch or TensorFlow; experience with MATLAB, C++, GPU computing, or parallel processing is advantageous.
• Experience with 3D/4D microscopy, multichannel or multimodal imaging, segmentation, tracking, image restoration, or quantitative phenotyping. Experience with electron microscopy, sparse annotations, or large volumetric datasets is particularly relevant.
• Ability to translate research prototypes into usable software, with experience in dataset curation, version control, testing, documentation, and reproducible workflows. Experience with Napari or other interactive scientific analysis interfaces is beneficial.
• Effective interdisciplinary communication, collaborative problem-solving, and experience mentoring researchers or teaching computational methods. Familiarity with cell biology, sensory neuroscience, organelle biology, spatial biology, or related biomedical applications is helpful.

Compensation

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