528 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions at Harvard University
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science, computational science and engineering, human-computer interaction, or a related area. We appreciate a record of teaching at the undergraduate or graduate level. Special Instructions Please submit
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desired. Special Instructions Required materials, to be submitted through the ARIeS portal (https://academicpositions.harvard.edu/hr/postings/15712 ): 1. Cover letter 2. Curriculum Vitae 3. Writing sample
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basis until the position is filled. For more information, please visit our website, https://topo.chemistry.harvard.edu/ . Please note: This position is contingent upon funding and satisfactory
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an inclusive community of dedicated problem-solvers who hold themselves - and one another - to the highest academic and professional standards. To learn more about us, please visit https://seas.harvard.edu
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by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW
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) is a community of Information Technology professionals committed to understanding our users and devoted to making it easier for faculty, students, and staff to teach, research, learn, and work through
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challenges to global public health. Learn more about MET and their research here: https://www.hsph.harvard.edu/molecular-metabolism/. The lab of Dr. Nora Kory studies compartmentalization of metabolism and
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information: http://www.hbs.edu/information-technology/about-us . Job Description Job Summary: This role offers an exciting opportunity to be at the forefront of technological transformation, supporting Harvard
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ecosystem of research, learning, and entrepreneurship that includes MBA, Doctoral, Executive Education, and Online programs, as well as numerous initiatives, centers, institutes, and labs, Harvard Business
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review