111 phd-in-architecture-interior-design-built-environment Fellowship positions at Harvard University
Sort by
Refine Your Search
-
communication skills, and enjoy working with diverse groups of people. A strong candidate can tackle challenging projects with minimal oversight in a fast-paced, environment while maintaining great attention
-
PhD in theoretical neuroscience, physics, computer science, or related fields is required. Applicants must demonstrate strong analytical and numerical skills. Additional Qualifications Special
-
Opportunities (for Harvard undergraduates) – Harvard University Center for the Environment Grants-In-Aid of Undergraduate Research – Arnold Arboretum of Harvard University, Museum of Comparative Zoology (MCZ
-
, and Johannes Stroebel – in a team-oriented lab environment, with regular lab meetings, seminars, and other events. Fellows also have the opportunity to support our research translation and policy work
-
appointment is required. Additional Qualifications The successful applicant will be expected to have a solid statistical background, research experiences showing independence and innovation, and demonstrated
-
. Depending on available funding, one of the positions may be specifically targeted at candidates interested in the scaling regimes of deep networks. Basic Qualifications A PhD is required. We seek candidates
-
While at UNC-Chapel Hill, 2022 AOB Postdoc Alexandria Bredar, PhD conducted research with a focus on contributing to a carbon-neutral, recyclable energy infrastructure, working to answer
-
, or plan to enroll in, a part-time program. Am I eligible for the International Fellowship? No. Only applicants who are enrolled in or plan to enroll in full-time programs or research are eligible. Am I
-
whose work innovatively engages with the environment and the humanities. In addition to pursuing their own research projects, fellows will be core participants in the bi-weekly seminar meetings for both
-
and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees