16 machine-learning scholarships at NTNU Norwegian University of Science and Technology in Norway

PhD in Dynamic Allocation of Distributed Resources in Short-term Reserve and Flexibility Markets

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

PhD Candidate in Trustworthy AI

data, model training, and model usage often attributed to disparate actors without established trust between them. This decentralization introduces vulnerabilities, as adversarial machine learning

PhD Candidate in Pulse Jet Propulsion for Commercial ships

propulsion, optimization, machine learning, simulations, or similar topics practice scientific communication by writing articles and attending conferences with other scientists and PhD candidates Investigate

PhD Candidate within AI and Big Data

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

PhD Candidate in developing a platform for computational battery design

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

PhD Candidate in AI and data-driven safety management in operation of autonomous ships

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

PhD Candidate in health-aware operation and maintenance planning

selection criteria Knowledge in statistical learning/machine learning, estimation techniques Experience with general process modelling Experience with methods from RAMS (Reliability, Availability, Safety

PhD position in climate change mitigation in the Aviation sector

true team player. Self-driven with a strong ability to work independently when required. Motivated to work in a multi-disciplinary and multi-cultural team, with an interest in cross-disciplinary learning

PhD Candidate in Advanced Forming of Extruded Aluminium Profiles with High-Content Recycled Alloys

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

PhD Candidate in Safety-aware predictive control methods with robust contingency plans

selection criteria Knowledge in statistical learning/machine learning, state estimation techniques Experience with process modelling Experience with methods from Reliability, Availability, Safety, Maintenance