Sort by
Refine Your Search
-
observational studies using the James Webb Space Telescope (JWST). The successful candidate will work with Prof. Daniel Eisenstein and collaborators on the analysis and interpretation of JWST data, with
-
a planned randomized experiment. The fellow will be involved in data cleaning, data analysis, design of the experiment, academic and policy writing. The candidate will work under the supervision
-
analysis and write up of an extensive longitudinal dataset that has followed a large population in Cebu, Philippines, for more than 40 years. Recently generated genome-wide methylation data will be used
-
machine learning methods as well as in biological data analysis are needed for the position. The postdoctoral researcher will play a leading role in this research, including methods development, data
-
and naturalistic visual paradigms in macaque monkeys, together with quantitative behavioral analysis and computational modeling, to identify circuit and population-level signatures of drug related
-
models, report formats and other analysis considerations, determine and write statistical considerations and algorithms for protocol documents according to study design and appropriate statistical methods
-
skills for behavioral and neural data analysis Additional Qualifications Demonstrated experience designing and executing behavioral or neurophysiology experiments, ideally in nonhuman primates Experience
-
to collaborate with colleagues in other Schools at Harvard, particularly Harvard Business School. Familiarity with forms of analysis that address inequality, race, gender, class, and social location is presumed
-
technologies. Experience with imaging technologies and computational data analysis preferred. Strong publication record and evidence of research independence. Excellent communication, teamwork, and problem
-
Genomics at Harvard Medical School Several positions are available in the Park Lab (https://compbio.hms.harvard.edu/ ). The aim of the laboratory is to develop and apply innovative computational methods