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for hypersonic wind tunnels, under the direction of Dr. Samuel Grauer. Job Duties: Include refinement of the neural-implicit LAT (NILAT) algorithm. Hardware design. Deployment at NASA facilities. The role combines
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investigate AI/ML algorithms to deliver behavioral interventions at the moments of need and how those can/should be modified to account for personalized preferences. To accomplish this goal, we study behavior
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the brain. We are particularly looking for a PhD level systems neuroscientist with expertise in animal behavior tracking using deep learning algorithms and its causal link with specific neural circuits
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%): Generating academic outputs, such as presentations, grants, and manuscripts. 4) Related tasks (10%): Other related tasks assigned by the principal investigator. Eligibility: Candidates holding a PhD degree in
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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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research involving biological data analysis and modeling of biological systems. In particular, they will develop and apply algorithms to construct discrete dynamic models of signal transduction networks
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reporting. Candidates must have a PhD in MatSE and experience in mechanics of materials, computational modeling, and experimental characterization of materials. Candidates should submit a CV and cover letter
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. Candidates must have a PhD in MatSE and experience in developing phase-field models of microstructures and properties by start date. This term position is funded for one year from the date of hire with
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occupant harm from exposure to indoor bioaerosols. Key Responsibilities Responsibilities include, but are not limited to: Developing and analyzing new HVAC control algorithms to balance energy efficiency and
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of the following: quantum field theory, quantum information, out-of-equilibrium dynamics, theoretical cosmology, particle physics, gravity, or quantum algorithms. Candidates are expected to demonstrate