81 deep-learning-phd-"Computer-Vision-Center" Postdoctoral positions at Stanford University
Sort by
Refine Your Search
-
subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
-
studies to identify targets for medical intervention and to generate insight that meaningfully impacts patient care. This position provides direct mentorship from faculty with deep expertise in neuroimaging
-
postdoctoral scholar to aid in a pilot clinical trial using a novel deep brain stimulation approach for cognitive and cognitive-motor dysfunction in Parkinson’s disease. The goal of the research in
-
approaches to remove atmospheric particulate (e.g., PM2.5) pollution. The math-based subgroup focuses on the use of deep learning and generative AI to address critical problems for the electric grid and broad
-
fellowships and NSF SBE postdoctoral awards. We especially welcome applicants with theoretical interest in child language development, strong computational and analytical skills (deep learning frameworks), and
-
of the selected candidate, budget availability, and internal equity. The Fellow will teach one course per year in the Department of Religious Studies at Stanford, give one talk on their research during the term
-
, clinical trial of behavioral treatment for children with obesity and their families. This position provides an outstanding opportunity to build unique and deep experience and expertise in designing
-
, and MRV performance) and identify optimal deployment models coupled with learnings from forest management. Conduct techno-economic and life-cycle assessments (TEA/LCA) integrating forest operations
-
PhD graduates who are passionate about leveraging computational methods to transform trauma and acute care surgery. Fellows will work at the intersection of clinical medicine, data engineering, and
-
the resulting data from the experiments. Required Qualifications: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including