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Field
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spatial memories and how these processes are influenced by addictive substances. You will use state-of-the-art in vivo two-photon imaging and electrophysiology to record neuronal activity in rodents
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time
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knowledge for a better world. You will find more information about working at NTNU and the application process here. ... (Video unable to load from YouTube. Accept cookie and refresh page to watch video
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sustainable machine learning approaches and addressing renewable energy related projects. Likewise, deploying a novel paradigm of KGML (knowledge guided machine learning) can propel further research. PhD
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relate to processing-relevant traits such as fry quality, yield, and waste? The project will take an integrated approach combining advanced imaging, quantitative phenotyping, and genetics. A high
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groups, and collaborative writing processes. Your qualities The ideal candidate for this PhD position must have: a Master’s degree in a relevant scientific discipline in the Humanities or Social Sciences
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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About Us You will join an international and highly
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force behind the design, construction, and experimental validation of a next-generation magnetocaloric heat pump prototype, a technology poised to revolutionize the market and contribute significantly
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underlying these processes remain poorly represented. This PhD project aims to fill that gap by using molecular simulations to quantify the interactions of SOM and EPS with water and minerals, and to connect
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/processes; formalise requirements; prototype traceability. Expected outputs include requirement templates and an initial workshop paper. Year 2: Integrate PlaTFoRm tools; evaluate coverage and fault