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- NTNU Norwegian University of Science and Technology
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requirements for admission to the PhD programme Experience implementing and modifying deep learning architectures. Working knowledge of Python and a modern deep learning framework. Strong programming skills in
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considered an advantage. Candidates should have programming skills in Python, R, or similar languages and be motivated to develop advanced modelling tools for sustainable transport planning. Your master’s
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, specifically with Python-based engineering software development and hardware interfacing. Excellent oral and written English skills Demonstrated ability to independently conduct engineering projects Demonstrated
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validating mechatronic systems. Experimental methods and testing Programming and software development, specifically with Python-based engineering software development and hardware interfacing. Excellent oral
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/genome biology, minimum grade B (ECTS grading scale) or equivalent. The Master’s degree must include a thesis of at least 30 ECTS. Programming skills in Python, R or equivalent scientific computing
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. Python, MATLAB). You will have the opportunity to participate in digital Norwegian language courses during your employment. Advantageous knowledge and skills Experience of working with SuperDARN and EISCAT
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learning Solid programming skills, preferably in Python and modern deep-learning frameworks such as PyTorch Experience working with large or complex datasets The applicant must be fluent in English, both
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and written communication skills in English. Desirable qualifications Documented Experience with Python and/or another scientific programming language. Documented Experience with image processing
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/genome biology, minimum grade B (ECTS grading scale) or equivalent. The Master’s degree must include a thesis of at least 30 ECTS. Programming skills in Python, R or equivalent scientific computing
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, longitudinal modelling, machine learning and multivariate approaches. Proficiency in programming (e.g., MATLAB, Python, or R), handling large datasets, and working with complex analysis 2 pipelines is an