Postdoctoral Image Processing Jobs in Boston, MA

Location: Boston University School of Medicine (Boston, MA)
Supervisors: Jesse Mez, MD, MS; Jon Cherry, PhD; Vijay K. Kolachalama, PhD
Position summary

We seek a motivated postdoctoral research fellow to join an interdisciplinary team investigating post traumatic neurodegeneration using digital neuropathology whole slide images (WSIs), clinical phenotypes, and machine learning. The fellow will play a central “bridge” role between neuropathology, clinical neurology, and data science, working across brain bank cohorts (UNITE, Framingham Heart Study, and the BU ADRC) to develop and validate computational classifiers and to link image derived pathology features with lifetime clinical data. The position is ideal for a candidate with a strong neuroscience background—particularly in head trauma and/or neurodegenerative disease—who wants to combine wet lab neuropathologic techniques with computational model development and translational analyses.
Project overview

This NIH funded project leverages WSIs and richly phenotyped cohorts to: develop algorithms to detect neuropathologic signatures of chronic traumatic encephalopathy (CTE); identify neuropathologic features associated with repetitive head impact (RHI) exposure; and map pathology patterns to clinical outcomes observed in life (cognitive impairment, behavioral dysregulation, parkinsonism, etc.). The fellow will help build and curate image datasets, generate and validate annotations and experimental labels, design and apply machine learning/image analysis pipelines, and integrate pathology features with clinical and epidemiologic data across cohorts to address these aims.
Key responsibilities

• Lead development, evaluation, and refinement of machine learning and image analysis pipelines for WSIs, including preprocessing, annotation workflows, model training/validation, and interpretability analyses.
• Curate and harmonize WSI datasets and associated metadata from multiple brain banks; contribute to quality control
• Design and perform targeted wet lab neuropathologic experiments (e.g., immunohistochemistry, staining optimization, region specific sampling) to validate computational findings and generate ground truth labels as needed.
• Integrate pathology derived quantitative features with clinical and cohort data to test associations with RHI exposure and clinical phenotypes; collaborate on statistical analyses.
• Collaborate closely with neuropathologists, clinicians, and computational scientists to interpret findings in a neuropathologic and clinical context, and iteratively improve models.
• Maintain reproducible, well documented workflows (code, pipelines, notebooks) and manage data in accordance with BU policies and grant requirements.
• Disseminate results through manuscripts, grant reports, and presentations; assist with mentoring trainees and coordinating with collaborators.

Application instructions
Please submit a single PDF to Dr. Jesse Mez at jessemez@bu.edu containing:

1. Cover letter describing your interest and relevant experience across neuropathology, neurology, and data science, and how you envision bridging wet lab and computational work.
2. Curriculum vitae (with publication list).
3. Contact information for three references (one should be your PhD/postdoc advisor or equivalent).
4. Representative papers, or other relevant work samples.

Review of applications will begin immediately and continue until the position is filled.

Qualifications
Required

• PhD, MD/PhD, or equivalent doctoral degree in neuroscience, neuropathology, computational biology, biomedical engineering, computer science with neuroscience focus, or closely related field.
• Demonstrated neuroscience background with interest in head trauma and/or neurodegenerative disease.
• Experience in at least one of the following: neuropathologic methods (histology/IHC), digital pathology/WSI workflows, or machine learning for biomedical images.
• Strong written and oral communication skills and proven ability to work effectively in interdisciplinary teams.
• Track record of scholarly productivity (publications, preprints, or substantial project deliverables).

Preferred

• Programming proficiency in Python and/or R; experience with deep learning frameworks (PyTorch, TensorFlow) preferred.
• Prior experience working with whole slide image formats, slide scanners, annotation tools (HALO, QuPath, ASAP, SlideRunner, etc.), and image pre processing.
• Experience integrating multi modal datasets (pathology images, clinical, epidemiologic cohorts).
• Familiarity with neuropathologic features and proteinopathies (tau, TDP 43, amyloid, alpha synuclein).
• Hands on wet lab histology or IHC experience.
• Prior involvement with brain bank data or large longitudinal cohort studies.

We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, natural or protective hairstyle, religion, sex, age, national origin, physical or mental disability, sexual orientation, gender identity, genetic information, military service, pregnancy or pregnancy-related condition, or because of marital, parental, or veteran status. We are a VEVRAA Federal Contractor.