POSTDOCTORAL SCHOLAR POSITION: GENERATIVE AI FOR HEALTH AND MEDICINE (SHAH LAB)

Position Overview

Salary range: The salary range for this position is $66,737-$80,034. The posted UC salary scales set the minimum pay determined by experience level. See: Postdoc Scholar Salary Scale
Effective 10/1/26: The salary range for this position is $71,491-$85,736. See: Postdoc Scholar Salary Scale
Application Window
Open date: July 1, 2026
Next review date: Saturday, Aug 1, 2026 at 11:59pm (Pacific Time)
Apply by this date to ensure full consideration by the committee.
Final date: Wednesday, Jun 30, 2027 at 11:59pm (Pacific Time)
Applications will continue to be accepted until this date, but those received after the review date will only be considered if the position has not yet been filled.

Position Description

The Computational Medicine Research Group led by Prof. Pratik Shah at the University of California, Irvine, invites applications for a Postdoctoral Scholar position. The lab seeks outstanding PhD or MD, PhD applicants with strong academic backgrounds in computer science, biomedical informatics, biomedical engineering, statistics, or related fields. The lab is engaged in developing novel deep learning and AI-based technologies for digital biopsies from medical images and real-world clinical decision-making from non-imaging datasets, with research published in top journals such as Cell Reports Methods, Nature Digital Medicine, JAMA, IEEE Conferences, and Proceedings of National Academies of Science Engineering and Medicine Workshops. Selected candidates will have the opportunity to train for publishing in leading biomedical journals and machine learning conferences, networking with government funding agencies, industry partners, foundations, and academic experts. Training in fellowship writing, teaching/mentoring, oral presentations and review of manuscripts will be provided. For more information about the research group, publications, projects, and Prof. Shah, please visit: Shah Lab ; About Prof. Shah

Responsibilities

• Collecting, preprocessing, and visualizing high-dimensional medical images, non-imaging clinical (EMR), and genetic sequencing datasets
• Training and validating generative deep learning (e.g., GANs, Diffusion, Transformers) and deep reinforcement learning models
• Focusing on generating and validating diagnostic tools for clinical use
• Developing novel statistical models for uncertainty quantification, causality estimation, and prediction accuracy
• Publishing research in leading biomedical and machine learning journals and conferences
• Engaging with industry partners, government agencies, and academic experts
• Strong analytical, organizational, and communication skills
• Committed to mentoring and teaching

Qualifications

• Basic qualifications (required at time of application): PhD or MD, PhD in computer science, biomedical informatics, engineering, statistics, or a related field either at the time of application or be working towards the PhD.