Machine Learning / Artificial Intelligence / Precision Medicine Postdoctoral Fellow

The Artificial General Intelligence (AGI) Laboratory at Mayo Clinic (Rochester, MN, USA), led by Dr. John Kalantari, invites applications for postdoctoral positions focused on the development and use of novel machine learning methodologies to solve fundamental problems in biology, personalized medicine and artificial general intelligence. We have multiple openings for postdoctoral researchers to conduct basic and applied research in Reinforcement Learning for optimal sequential decision-making under uncertainty, and Probabilistic Machine Learning on real-world problems in healthcare, including cancer and rare/undiagnosed disease applications.

We are seeking highly motivated research engineers with a strong record of accomplishment in probabilistic machine learning and causal inference. Candidates will join our efforts exploring three research directions: 1) extracting information from large scale multi-omic datasets (genomics, epigenomics, proteomics, microbial, metabolomics, transcriptomics, pharmacogenomics) and integrating it with other data modalities. 2) developing reinforcement learning, meta-learning, transfer-learning, one-shot learning, and active-learning algorithms to drive biological discovery. 3) designing explainable and interpretable generative models to facilitate the formulation of new hypotheses for treatments or other clinical studies. The vast amount of biomedical data generated by the Mayo Clinic demands novel statistical and machine learning models to extract, analyze and integrate information autonomously in a scalable manner.

Postdoctoral researchers will be mentored by Dr. John Kalantari and other faculty investigators in the Center for Individualized Medicine at the Mayo Clinic. The Center is a vibrant, inter-disciplinary, well-funded environment, with strong mentoring and training opportunities for individual career development. In addition, the candidate will have access to a wealth of resources including high-quality exabytes of multi-modal biomedical data, a state-of-the-art high-performance computing facility, as well as unlimited access to the Google Cloud GCE environment for large-scale deployment of AI/ML architectures on SOA CPUs, GPUs and TPUs.

Successful applicants will have or be near completing a Ph.D. in computer science, engineering, statistics or a related area, and will have extensive research experience and a strong publication record in statistics, probability, machine learning and/or artificial intelligence. Preference will be given to applicants with some experience in large-scale modeling with Bayesian methods, non-parametric Bayesian models, approximate inference and experience with reinforcement learning.

The applicant must be strongly familiar with programming languages commonly used in data science (e.g. Python, Java, C, C++), and comfortable with learning/applying standard bioinformatics tools for processing and analyzing ’omics datasets. Excellent written and verbal communication skills, strong ability to independently and creatively solve research problems, while working effectively with the principal investigator and lab colleagues, is a requirement.

If you are interested in joining, please email Dr. John Kalantari (Kalantari.John[at]mayo[dot]edu) your CV, contact information of three individuals who can provide strong reference letters, PDFs of 3 of your representative publications, and a 1-page personal statement describing: your general research interests, previous experience, career goals, and specific research direction you’d like to pursue in our lab.