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preparation and testing or powder mixtures, and then to devise predictive models (possible using machine learning approaches) for the estimation of mixture properties from pure component propeties. The PhD
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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 days ago
in environmental or territorial studies. Preference will be given to candidates with aptitude for working in disaster risk management environments supported with GIS and Machine Learning, basic
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits
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to the lack of accurate models, machine learning-based problem solving is now revolutionizing almost every field of science and technology. FuturoChrom aims at developing model-free, purely reinforcement
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Professorhip grant, which you can learn more about here: https://www.cnap.hst.aau.dk/lundbeck-professorship As a PhD fellow your tasks include: Conduct research under the supervision of senior CNAP staff members
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& Solar Power Plants Battery Energy Storage Systems (BESS) Smart Grids & Power Systems Sustainable buildings, communities and cities[EC1.1] Artificial Intelligence & Machine Learning for Energy Digital
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biomedical engineering, electrical engineering, machine learning, statistics, computer science, or a related area considered relevant for the research topic, or completed courses with a minimum of 240 credits