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, (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 ACP’s captured instances
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systems, statistical physics and machine learning, and using these insights to develop new methods, with the support of competent and friendly colleagues in an international environment? Are you looking
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reconstruction - Estimation theory - computational methods and deep learning approaches. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7249-HERRIG-026/Default.aspx Work Location(s) Number
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: 9677 Job Summary: The Gildart Haase School of Computer Sciences and Engineering (GHSCSE) is looking for an adjunct to teach INFO_1101- Computer Concepts and Technology in the Fall 2025 semester. This is
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/Planning Internal Number: 7048624 Part Time Lecturer - Architecture About the Opportunity The Lecturer will teach introductory courses in architectural drawing, sketching, studio design, computer modeling
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Research Infrastructure? No Offer Description Based at Australian Institute Machine Learning (AMIL), City East Campus 2 x Full-time, 2-year fixed term contracts Salary Range: $114,917 - $135,932 per annum
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for supply chain and marketing optimization. The project will integrate machine learning, deep learning, foundation models, and interpretable AI approaches, ensuring scalability, robustness, and industrial
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status, childbirth, citizenship status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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position in machine learning (ML). This position offers a unique opportunity to conduct both basic and applied research in concert with collaborators working on diverse scientific and security problems