Overview

Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.

The successful candidate will lead research in machine learning method development and applications of the methods to some of the most impactful datasets.

Biomedical data involve rich interactions that span from the molecular level to the level of connections between diseases in a patient and to the societal level encompassing all human interactions. These interactions at different levels give rise to a bewildering degree of complexity, which is only likely to be fully understood through data-driven and computationally enabled study. This scientific approach not only opens up new avenues for understanding nature, analyzing health, and developing medicines to help people but can impact on the way predictive modeling is performed today at the fundamental level.

Among others, possible research projects include:

• Machine learning for biomedical data in efforts to set sights on new frontiers in genomics, drug discovery, and precision health beyond classic neural networks on image and sequence data.
• Representation learning, embedding methods, and graph neural networks in efforts to bridge the divide between research data and patient data.
• Fusion, learning and reasoning for knowledge graphs in efforts to combine biomedical data in their broadest sense, reduce redundancy and uncertainty, and make actionable predictions.
• Next-generation algorithms for networks, focusing on large networks of interactions between biomedical entities and their applications to network biology and medicine.
• Contextually adaptive AI in efforts to advance algorithms to train more with less data and reason about never-before-seen phenomena as algorithms encounter new patients, diseases, or cell types.

Qualifications

We seek highly-motivated applicants with background in one or more of the following areas: machine learning, explainable AI/ML, computational healthcare, and network science. Successful applicants will be strong technically as well as have an inclination towards real-world problems.

We are looking for applicants with demonstrably strong research skills, ideally, with multiple publications in top venues in machine learning, artificial intelligence, and bioinformatics (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI, Bioinformatics, Nature Methods), and/or top-tier interdisciplinary journals.

Candidates must have a Ph.D. or equivalent degree in computer science, statistics, biomedical informatics, computational biology or a closely related field. Strong programming skills and experience with machine learning frameworks and/or its applications to biology and medicine are required.

Application process

The position is available immediately and can be renewed annually. Interested applicants should submit the following documents via email to Prof. Zitnik and use the subject line “Postdoctoral Fellowship Application in biomedical AI”:

• Curriculum Vitae (please include links to your academic webpage and any software you developed, e.g., GitHub repositories)
• Two representative publications (preprints are acceptable)
• Statement of Research (2 pages) describing prior research experience and future research plans
• Three letters of recommendation (will be solicited after the initial review)

We are currently reviewing applications for this position. Interested candidates are encouraged to submit their applications as soon as possible.

Advisor

Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik is a computer scientist studying machine learning, focusing on challenges brought forward by data in science, medicine, and health. Before Harvard, she was a postdoctoral fellow in Computer Science at Stanford and also a member of the Chan Zuckerberg Biohub.

Dr. Zitnik has published extensively in top ML venues (e.g., NeurIPS, ICLR, ICML) and leading interdisciplinary journals (e.g., Nature Methods, Nature Communications, PNAS). She has organized numerous workshops and tutorials in the nexus of AI, deep learning, drug discovery, and medical AI at leading conferences (NeurIPS, ICLR, ICML, ISMB, AAAI, WWW), where she is also in the organizing committees. She also organized the National Symposium on drugs for future pandemics on behalf of the NSF.

Dr. Zitnik’s algorithms have had a tangible impact, which has garnered the interests of government, academic, and industry researchers and has put new tools in the hands of practitioners. Her methods are used by major institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, Massachusetts General Hospital, and the pharmaceutical industry.

Dr. Zitnik’s research recently won best paper and research awards from the International Society for Computational Biology, Bayer Early Excellence in Science Award, Amazon Faculty Research Award, a Rising Star Award in EECS, and a Next Generation Recognition in Biomedicine, being the only young scientist who received such recognition in both EECS and Biomedicine.

Harvard is an Equal Opportunity Employer.