134 machine-learning "https:" "https:" "https:" "https:" "https:" "Cardiff University" positions at Nature Careers in Denmark
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. They will develop new curricula and teach classroom-based as well as field-based courses and supervise students at all academic levels. Depending on research field, interest in collaborating with industrial
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scientific interests and priorities and they will establish a vibrant research group utilizing external funding. They will develop new curricula and teach classroom-based as well as field-based courses and
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5-year undergraduate nanotechnology programme and nanoscience graduate programme (https://phd.nat.au.dk/programmes/nanoscience/) the center provides a full educational environment. In
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Tenure-track Assistant and Associate Professorship positions in Algorithms at the Department of M...
interested in learning more about the position, please contact Head of the Algorithms Section Kim Skak Larsen at kslarsen@imada.sdu.dk. Conditions of employment Appointment as a Tenure Track Assistant
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: Application (cover letter) Vision for teaching and research for the tenure track period CV including employment history, list of publications, H-index and ORCID (see http://orcid.org/ ) Teaching portfolio
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see: http://ecos.au.dk/en/ . What we offer The department offers: A multi-disciplinary research environment collaboration within strong research teams with extensive experience in carbon flux research
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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: Advanced deep learning architectures Mathematical foundations of machine
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qualifications include: Ph.D. in Computer Science, Computer Engineering, Electrical Engineering or a related field; Strong background in Deep Learning (e.g., Transformers, foundation models); Strong programming
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The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we are seeking a highly talented and motivated PhD student within the field
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials