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quantitative focus on these fields Solid foundation in statistics and/or machine learning, e.g., supervised learning, regression modeling, model evaluation, or high-dimensional data analysis Good programming
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include: (1) implementing light–matter interaction in CFD via the radiation transport equation and suitable attenuation models; (2) integrating kMC-based surface kinetics through machine-learning surrogate
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the Research Promotion Foundation, RIF, (EXCELLENCE/0524/0337), Title: “Machine Learning for Intelligent Insect Monitoring” and proposes an automated early warning system that will be able to detect and classify
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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will be integrated with statistical and machine-learning methods to classify polarity states and identify quantitative signatures predictive of metastatic behavior. The project will deliver transferable
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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Polytechnique de Paris. The group conducts research at the intersection of statistical learning, machine learning, and data science, with a strong focus on structured data, representation learning, and
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expertise in artificial intelligence (AI), machine learning (ML), and data science. The position will be a part of the Walk Tall research team based at BC Children’s Hospital. The Postdoctoral Fellow will
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or Phonetics Basic knowledge of machine learning tools; familiarity with a scripting language Ability to communicate and coordinate with different partners: field linguists, computer scientists, engineers
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deadline Experience with urban acoustic monitoring or transportation noise assessment Programming skills in Python Knowledge of machine learning techniques applied to acoustic or environmental data