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world. The Electrical and Computer Engineering Department and NYU Nanolab host a growing interdisciplinary research program in superconducting and semiconductor-based quantum technologies, with strong ties to national
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will work on an NIH-funded project developing an ethical framework for participant engagement in long-term implanted brain computer interface trials, have the opportunity to collaborate with neural
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must have the following essential skills: Strong background in plant breeding and genetics Strong computer skills and proficiency in R or other programming languages Outstanding interpersonal and
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science, electrical and computer engineering, sociology, public policy, information science, communication, economics, political science, psychology, philosophy, and related technology disciplines. Selected candidates
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techniques; large data processing and high performance computing; advanced causal inference and statistics; computer vision and novel applications of machine learning. Advanced knowledge of R or Python is
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environment consisting of mathematicians, computational and computer scientists, and domain scientists conducting basic and applied research in support of ORNL’s mission. Specific responsibilities include
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. Preferred Qualifications: Experience in decision analysis and developing computer-based simulations to model either infectious or non-infectious diseases. Evidence of research productivity in mathematical
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computational experiments designed by mathematicians on the team • Interface with mathematicians and applied computer scientists to demonstrate the viability of novel neural networks on challenging benchmark
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University (https://aihub.osu.edu ). The AI(X) Hub at The Ohio State University is a university-wide initiative to accelerate research, innovation, and education in artificial intelligence. It spans 15
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at the intersection of active transportation safety and AI (e.g., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision