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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
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ESSENCE: Efficient Self-Supervised Machine Learning for Adaptive Wireless Communication Systems This project investigates self-supervised learning (SSL) for wireless communication systems to improve
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establishedDogra Lab (https://mann.usc.edu/faculty/dogra/ )at USC invites applications for two Postdoctoral Scholar Research Associates at the intersection of artificial intelligence (AI), mechanistic modeling, and
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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with scientific machine learning approaches (e.g., physics-informed neural networks, neural ODEs, operator learning) and their application to dynamical systems. Track B – Mechanistic Modeling
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) for data analysis, numerical modeling, or machine vision Supervision Exercised This position carries a high degree of independence. The Postdoctoral Associate will meet with the PI at least twice per week
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, regression models or machine learning. Applicants from clinical hepatology with experience in the above fields can also be interesting. You will work in an interdisciplinary and international research
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participant outcomes. The project will use a variety of approaches, including human perceptual experiments, machine learning, digital signal processing, and computational models of hearing. UConn has a vibrant