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Field
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students regulate their learning across a semester: how they adapt their study behavior in response to performance feedback, when and why they disengage from cognitively demanding strategies such as
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Science, or Artificial Intelligence. Core competencies: • Strong knowledge of Python and machine learning (classification, anomaly detection, clustering) • Knowledge of the Semantic Web and knowledge
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selection criteria Knowledge of sensors and measurement techniques Experience from the industry providing sails for merchant ships Experience with Computational Fluid Mechanics Knowledge of machine learning
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Quantum Development", formerly "Women In Quantum Development", https://www.wiqd.nl/ ). Job requirements We are looking for a motivated and creative researcher interested in working at the intersection
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. For more information about LIACS, visit our website. What you bring A MSc degree in Computer science, Artificial Intelligence, or a related field; Good knowledge of and experienced with Machine learning
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flat interconnect AI compute clusters. Machine learning clusters and artificial intelligence (AI) training have become increasingly popular in recent years. The recent introduction of OpenAI’s ChatGPT
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Inria, the French national research institute for the digital sciences | Villeurbanne, Rhone Alpes | France | 28 days ago
learning, privacy, security and distributed systems. Where to apply Website https://jobs.inria.fr/public/classic/en/offres/2026-10411 Requirements Skills/Qualifications We are looking for a candidate with
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, relating to craniofacial identification research and machine learning. You will require a computer science background. You will be applying AI and/or machine learning to Face Lab processes in relation
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development work at the Norwegian University of Science and Technology (NTNU) for general criteria for the position. Preferred selection criteria Experience with machine learning and neural networks Basic
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or environmental engineering, Mathematics (Operations research) or Computer Science or Machine Learning). Documented knowledge of relevant methodologies, both quantitative and/or qualitative, at master’s level