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Field
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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research. This exciting position offers a unique opportunity to develop cutting-edge Knowledge Guided Machine Learning (KGML) methods informed by ecosystem modeling of agroecosystem carbon and nitrogen
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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inference and statistics; computer vision and novel applications of machine learning.Advanced knowledge of R or Python is required. Intermediate knowledge in C/C++ and/or at least one SQL dialect is
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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of the top private universities in the United States, and the Tandon School of Engineering, located in Brooklyn, NY, is deeply committed to excellence in teaching and learning. Tandon fosters student and
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 5 days ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director
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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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catalytic turbomachines—compact devices that combine chemical reaction and flow functions—using a novel machine-learning-based method, ChemZIP, to accelerate the modelling of complex catalytic and gas-phase