Postdoctoral Positions in the Lab of Marinka Zitnik

Areas of Focus

Machine learning for drug discovery and development

Overview

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

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. 

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 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.

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”:

  1. Curriculum Vitae (please include links to your academic webpage and any software you developed)
  2. Two representative publications (preprints are acceptable) 
  3. Statement of Research (2 pages) describing prior research experience and future research plans
  4. 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

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Representation learning and embeddings for biomedical networks and knowledge graphs

Summary

The Department of Biomedical Informatics (DBMI) at Harvard Medical School and the Zitnik Lab are looking for a Postdoctoral Fellow for research in novel biomedical machine learning and data science methods. 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. We are investigating machine learning for biomedical sciences, focusing on new methods for learning and reasoning over rich interaction data and on translation of these methods into solutions for biomedical problems. 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:

  • Representation learning for biomedicine in an effort to set sights on new frontiers in genomics, drug discovery, and precision health beyond classic applications of neural networks on image and sequence data.
  • Network embedding methods in an effort to bridge the divide between basic science and patient data.
  • Fusion of diverse data into knowledge graphs in an effort to combine biomedical data in their broadest sense to reduce redundancy and uncertainty and make them amenable to analyses.
  • 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 an effort 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 a highly-motivated candidate with strong research skills and background in machine learning and/or applications in bioinformatics and computational medicine. Candidates must have a Ph.D. or equivalent degree in a field relevant to biomedical data science, such as computer science, statistics, engineering, biomedical informatics or computational biology. Strong programming skills and experience with large-scale data analysis and deep learning frameworks are a plus.

Terms

The position is available immediately and can be renewed annually.

How to apply

Submit your application with a letter indicating your research interests and experience, a CV, names and email addresses of 2-3 references, and copies of two representative publications via email to marinka@hms.harvard.edu. We encourage applicants also to include links to any software they have developed.

Harvard Medical School is an Equal Opportunity/Affirmative Action Employer. Women and minorities are especially encouraged to apply.