Postdoctoral In Reinforcement Learning Jobs in Fullerton, CA

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
The candidate will contribute to two key research areas:

1. Generative AI for Medical Imaging and Digital Biopsies Develop and interpret deep neural networks (DNNs) for automating non-destructive tissue-based analyses using high-parameter medical images (e.g., pathology, MRI, CT, and RGB) and molecular profiles. Conduct hypothesis-driven research to link molecular profiles to disease biology. Focus on generating and validating diagnostic tools for clinical use.
2. Generative and Predictive AI for Clinical Decision Support and Statistical Inference Develop biologically informed statistical methods and uncertainty estimation models to train deep learning models for clinical decision-making from EMRs and genetic sequencing data. Focus on predicting patient outcomes and discover novel biologically relevant disease subtypes.

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
* 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
Preferred Qualifications
* Familiarity with data processing techniques for medical imaging, time-series clinical EMR, and genetic sequencing data
* Previous experience leveraging deep learning libraries (e.g., OpenCV, Theano, Caffe, Keras, TensorFlow) and machine learning models/datasets (e.g., AlexNet, ImageNet, MNIST, MySQL, MongoDB)
* Experience with probabilistic models, Bayesian methods, reinforcement learning, and uncertainty quantification from imaging and non-imaging clinical EMR and genetic sequencing data
* Expertise in programming (Python, MATLAB, C++, Java) and software development in a collaborative environment using version control (Git, GitHub), issue tracking, and code review
* Track record of writing and publishing first-author research papers in peer-reviewed journals or top machine learning conferences

Department: https://medschool.uci.edu/research/clinical-departments/pathology-laboratory-medicine

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.

• 3 required (contact information only)

The University of California, Irvine is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected categories covered by the UC anti-discrimination policy.
As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• "Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment or discrimination, as defined by the employer.
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As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.