39 machine-learning "https:" "https:" "https:" "https:" "https:" "U.S" positions at University of Washington in United States
Sort by
Refine Your Search
-
, robotics, biomechanics and biomedical engineering sciences, physics-based machine learning and artificial intelligence modeling and controls, energy storage and electrification, renewable energy and thermal
-
morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities
-
learning come together, the opportunity to network with other practitioners in different specialties and to continuously learn about new cutting-edge therapies • All activities of Pharmacy Technicians
-
include experience with fiber sensing, machine learning tools, and big data workflows. Instructions To apply, candidates will submit materials via Interfolio, comprising (1) a letter of interest describing
-
application development (containers and containers orchestration, machine learning, function as a service) Secure web application development practices Lean/Agile software development methodologies Work in
-
advanced manufacturing, including composite materials manufacturing, biomechanics and biomedical engineering sciences, physics-based machine learning and artificial intelligence modeling and controls, energy
-
in Robotics, including its application to advanced manufacturing, repair and non-destructive testing of composite materials, biomechanics and biomedical devices, physics-based machine learning and
-
teach students and trainees, including medical students and medical genetics residents through participation as part of the medical genetics courses and supervision as an attending. Compensation The base
-
month, commensurate with experience and qualifications or as mandated by a U.S. Department of Labor prevailing-wage determination. Other compensation associated with this position may include a moving
-
, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment