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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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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
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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
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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
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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
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(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
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. This work involves developing novel techniques, algorithms, and software packages that enable more robust and scalable approaches to cybersecurity using AI-based techniques. In addition to technical
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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
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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the complexities of the human regulome through advanced cell-free DNA profiling and developing cutting-edge computational algorithms and molecular profiling techniques. Our research focuses on early cancer detection