Sort by
Refine Your Search
-
experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be: highly motivated with a record of high scientific productivity, possess
-
, Machine Learning Engineers, Data Scientists, and Cloud Platform Engineers, you will build production-ready AI solutions that drive personalized engagement, expand educational reach, and support new learning
-
Institute or working on machine learning, artificial intelligence, or computational neurobiology at Harvard. A research proposal of no more than 3 pages (1500 words, exclusive of references) outlining plans
-
an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
-
an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
-
. Develop learning exercises, hands-on activities, workshops, and demos on various new and emerging topics such as AI, machine learning, embedded systems, and digital fabrication techniques. Collaborate and
-
of outstanding academic achievement. Indication of independent research experience and/or applied experience. Methodological interests in econometrics, statistics, machine learning, industrial
-
or Ecology, Bioinformatics, Data Science, Biostatistics, or a related field. 5+ years experience in big data analytics, statistical modeling, and machine learning. 5+ years direct experience working with
-
fellow. The ideal candidate will have research experience in the economics of the auto market, especially electric vehicles. The one-year position will be under the supervision of Professor James Stock and
-
laboratories in their machine learning endeavors. As the neuroscience community grows increasingly interested in using such expertise, having in-house resources for trainees to generate new models to test new