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- NTNU - Norwegian University of Science and Technology
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regulations that provides both incentives and constraints for the maritime energy transition and emission reduction. The research objective of the PhD is to develop models that capture the interaction between
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to ensure that the admission requirements are met, must be uploaded as an attachment. Main tasks Develop machine learning models to produce forest information at local and landscape scales Develop machine
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) at Norwegian University of Life Sciences (NMBU) has a vacant 3-year PhD–position related to developing deep learning models for 3D forest point clouds. The position is part of "SmartForest" (www.smartforest.no
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regulations that provides both incentives and constraints for the maritime energy transition and emission reduction. The research objective of the PhD is to develop models that capture the interaction between
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of Life Sciences (NMBU) has a vacant 3-year PhD–position related to developing deep learning models for 3D forest point clouds. The position is part of "SmartForest" (www.smartforest.no ), a center for
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management, utilisation of natural resources, shipping, predictive modelling, or climate risk Core courses in optimisation, microeconomics, scientific methods Elective courses in operations research
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, shipping, predictive modelling, or climate risk Elements from the two tracks can be combined. For more information about the Department of Business and Management Science and its research profile, visit
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will develop models to understand non-equilibrium transport of orbital angular momentum in superconducting hybrid structures. This is part of an effort to determine the merits of superconducting
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of quantum condensed matter physics and has a duration of three years. Your immediate leader will be the Head of Department. Duties of the position The candidate will develop models to understand non
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biology, field- and experimental work, bioinformatics and statistical modeling. The successful candidate will apply cutting-edge genomic tools to disentangle ecological adaptation and spatiotemporal trends