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