13 machine-learning PhD positions at NTNU Norwegian University of Science and Technology in Norway
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optimisation of aluminium extruded crash management systems (CMS). This research project combines operations research methodologies with machine learning techniques to revolutionise the design process, ensuring
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emerging field that applies computational techniques such as big data, machine learning, artificial intelligence, optimization, etc. for realizing sustainable future energy systems (smart grids, smart homes
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propulsion, optimization, machine learning, simulations, or similar topics practice scientific communication by writing articles and attending conferences with other scientists and PhD candidates Investigate
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, including comprehensive experience with modern machine learning libraries. Published scientific articles in the field of this position. Good written and oral English language skills Preferred selection
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data, model training, and model usage often attributed to disparate actors without established trust between them. This decentralization introduces vulnerabilities, as adversarial machine learning
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professionally relevant background with a master’s degree (or similar level) involving computational science (machine learning and modelling), and electrochemical experimentation on batteries and pouch cell
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areas: Machine learning; Reliability, availability, maintainability, and safety (RAMS); Marine/Control engineering education must correspond to a five-year Norwegian degree program, where 120 credits
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partners of NorwAI. Required selection criteria You must have a professionally relevant background in algorithms, machine learning, database systems, or data mining, with a research-oriented master’s thesis
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costly machining of the included parts. By utilizing primary aluminium produced with renewable electricity in combination with a high amount of PCS it will be possible to produce aluminium parts with a
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to an obtained doctoral degree. The PhD candidate will investigate fundamental issues on edge intelligence, with focusing on distributed learning and inference of foundation models in the IoT-Edge-Cloud continuum