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- NTNU Norwegian University of Science and Technology
- NTNU - Norwegian University of Science and Technology
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Field
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in cyber-physical systems are susceptible to breaches of security, safety, and liveness guarantees. To this end, a rigorous mapping of selected semantics found in manufacturing automation protocols as
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. Deep Learning techniques, Data Engineering, and Semantic Technologies Open-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data frameworks
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(SVA). Upon admission to the programme, the Doctoral Committee will assign the candidate a minimum of two supervisors. If the main supervisor is not employed at TKD or SVA or otherwise formally
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of an individual assessment. The master’s degree in question must formally qualify the applicant for admission to PhD programmes in the country in which the master’s degree was taken. Applicants must have written a
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collaboration with the Department. The remaining six months will be allocated to formal training. As a PhD candidate, you must participate in the Faculty of Humanities’ ph.d program program . The PhD programme
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degree and open up exciting career opportunities? As a PhD Candidate with us, you will work to achieve your doctorate to gain formal qualifications as researcher in engineering science. This opens up
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applications will be reviewed by the faculty, and applicants who do not meet the formal qualification requirements will not be forwarded for evaluation. The appointment will be in accordance with the principles
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, capacity for mathematical formalism. This can include your academic record, previous projects, work experience etc. Multi-disciplinary knowledge in the field Knowledge in agronomy, photobiology Knowledge
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. The research group is also associated with the NTNU Energy Transition Programme which arranges monthly Energy System Seminars and the Yearly Energy Transition Week at NTNU. These formal and informal
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with algorithms, numerical methods, signal processing and dynamic system modeling Experience from or formal training within analysis and processing of large data sets, signal processing is mandatory