Sort by
Refine Your Search
-
to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $86,100 Aligning Machine Learning Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to
-
gyroscope. Required Qualifications: Doctoral degree issued prior to appointment start date Strong background in photonics, especially lasers and sensors Required Application Materials: CV Stanford is an equal
-
highly motivated and collaborative Postdoctoral Fellow with an interest in optics, device physics, and/or environmental sensor integration for ocean observation. We are an interdisciplinary group
-
cloud computing tools and big data workflows A track record of completed research projects Well-written, peer-reviewed papers are expected Interest in using EHR metadata, wearable sensors, and process
-
it; as well as have theoretical skills including algorithm implementation/development and data visualization. Experience and interests include designing machine learning pipelines, building web
-
their different sensors for decision making. Humans will be able to seamlessly instruct these robots or directly collaborate with one or a team of robots. The fellow will have the opportunity to advise individuals
-
(intracranial and scalp EEG) and MRI data sets. Develop and implement algorithms for data processing and interpretation. Collaborate with clinicians and researchers to design studies and analyze results. Present
-
to uncover vulnerabilities in complex systems, bridging gaps between traditional testing techniques and emerging security threats. This work involves developing novel techniques, algorithms, and software
-
into tangible products. Critically, this work will generate a large open-source dataset of child-created games that can inform future designs of educational games and AI algorithms. The postdoctoral fellow will
-
systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user