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Field
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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vehicle validation. Design and implement machine learning models that capture the complexity and variability of real-world traffic situations, including unusual pedestrian behaviors, edge cases, and multi
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research, covering research topics including, but not limited to Crop Modeling, Machine Learning, Artificial Intelligence (AI), Internet of Things, and Big Data analysis for Precision and Prescriptive
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students. Qualifications: Required: PhD in Computer Science, AI, Data Science, Statistics, or related. Strong skills in machine learning and deep learning, with a fundamental understanding of LLMs
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research associate positions broadly in statistics and machine learning with Prof. Jason M. Klusowski (https://klusowski.princeton.edu ). The position is for one year with the possibility of reappointment
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parcellation (Glasser et al., 2016 Nature). The post-doc will be co-mentored by Matthew F. Glasser MD/PhD and David C. Van Essen PhD and be based in the Glasser/Van Essen laboratory in the WashU Radiology
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collaboratively, and a compelling explanation of how humanistic methods will be used in the completion of the year-long project. Candidates are expected to hold a PhD in a humanistic field at the time of
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
another, work together, and measure success. Basic Qualifications: A PhD in Physics, Materials Science, Chemistry, or closely related field completed within the last 5 years. Sound understanding of advanced
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities