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, using an array of methods including natural language processing and experiments. This is a two-year position (one-year contract renewable based on performance). The primary criterion for acceptance is
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, machine learning, or a closely related field Demonstrated expertise in visualization methods and technologies as applied to complex biomedical images Strong publication record and evidence of research
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DNA elements and transcriptional and chromatin remodeling machinery in gene regulation. More information about the lab and specific research areas can be found at https://adelman.hms.harvard.edu
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research tasks and projects, making use of selected methodologies (longitudinal designs, moderation and mediation, causal inference), library research (Pubmed searches, systemic review methods), and
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computational (bioinformatics) tools on human and mouse tissues and using in vitro methods on human cells, to explore the consequences of genetics variants on human biology. This is a multi-year position
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causal identification methods. There are no teaching requirements for these open positions. Basic Qualifications: A Ph.D. or equivalent degree in computer science, statistics, economics, management science
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environmental and agricultural economics topics and methods. Faculty mentors for this program will include Ishan Nath , Anna Russo , Wolfram Schlenker , and Charles Taylor . An important goal of the fellowship is
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/deployment. Causal Inference/Experimentation: Knowledge of experimental design, randomization, and causal identification methods. There are no teaching requirements for these open positions. Basic
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interpersonal and communication skills. While not a must, a strong background in computational methods and/or statistical methods is a plus. Special Instructions Applicants should submit a formal application and
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computational efforts across multiple labs at Harvard’s Faculty of Arts and Sciences and Medical School. As part of this effort, the Rubin lab is implementing new methods of studying aging in vitro using brain