536557 Blacksburg, Virginia, United States Biomedical Science Research / Scientific Veterinary Medicine Faculty (Salaried) Virginia Tech Job Description The Department of Biomedical Sciences and Pathobiology at the Virginia-Maryland College of Veterinary Medicine (Virginia Tech) is seeking a full-time (40 hours/week) Postdoctoral Associate in Computational Pathology. This position focuses on the development and validation of artificial intelligence-driven image analysis pipelines for digital pathology applications. Responsibilities Design and implement AI-driven image analysis pipelines for whole slide imaging (WSI), including convolutional neural networks (CNNs), visual transformers, and generative adversarial networks (GANs) Develop, optimize, and validate deep learning models for applications such as immunohistochemistry (IHC) quantification, segmentation, and phenotyping Perform rigorous model validation against expert pathologist annotations and diagnostic ground truth Collaborate closely with veterinary pathologists and interdisciplinary researchers to develop biologically and clinically meaningful computational tools Assist in data management, model documentation, and preparation of manuscripts and presentations related to the research program The ideal candidate will be highly motivated, capable of independent model development and validation, and enthusiastic about collaborating with pathologists to translate clinically relevant questions into robust computational solutions. Required Qualifications Ph.D. in computer science, data science, biomedical engineering, physics, or a closely related field. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining. Strong programming proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow Demonstrated expertise in machine learning, deep learning, and image analysis Ability to work independently while contributing effectively within a collaborative research environment Ability to stand, stoop, bend, walk for a considerable amount of time Preferred Qualifications Experience with whole slide imaging, digital pathology platforms, or AI applications in histopathology Familiarity with CNN-based architectures, segmentation approaches, phenotyping workflows, and/or visual transformers Experience working with microscopy or histopathology datasets and validating AI models against expert annotations Strong mathematical or computational modeling background Evidence of peer-reviewed publications in relevant fields

Pay Band Faculty Overtime Status Exempt:

Not eligible for overtime Appointment Type Restricted Salary Information Commensurate with Experience Hours per week 40+ Review Date 6/19/2026 Additional Information The successful candidate will be required to have a criminal conviction check. About Virginia Tech Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually. Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development. Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law. If you are an individual with a disability and desire an accommodation, please contact Christina Pacholec at pacholec@vt.edu during regular business hours at least 10 business days prior to the event.