Prof. Marinka Zitnik invites applications for a Postdoctoral Research Fellowship position at Harvard University.
The selected candidate will be expected to lead research in novel machine learning methods for knowledge graphs and graph representation learning. In addition, the candidate will also devise novel explainable algorithms and use them for applications in biomedical discovery, drug discovery and development, and therapeutics.
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 data mining (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI), and/or top-tier interdisciplinary journals (e.g., Nature family of journals, PNAS).
Candidates must have a Ph.D. or equivalent degree in computer science, statistics, or a closely related field. Strong programming skills and experience with machine learning and/or its applications to biology and medicine are required.
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”:
- Curriculum Vitae (please include links to your academic webpage and any software you developed)
- 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.
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.
Her research won Bayer Early Excellence in Science Award and numerous best paper and research awards from the International Society for Computational Biology. She was named a Rising Star in Electrical Engineering and Computer Science (EECS) by MIT and also a Next Generation in Biomedicine by Broad Institute of MIT and Harvard, being the only young scientist who received such recognition in both EECS and Biomedicine.
Harvard is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.