39 algorithm-"Multiple"-"U.S" "NTNU Norwegian University of Science and Technology" PhD positions in Netherlands
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, with three other modeling-focused PhDs who will work at different scales of assessment. This work is embedded into a larger team of PhDs, who are collecting data, working on multiple topics, from ecology
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characterized by the multidisciplinary background of its staff (criminal law, criminology, sociology, anthropology, public administration, psychology), often adopts an empirical perspective and applies multiple
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in scientific methodologies that have transformed the study of, and interest for bioarchaeological remains, makes the PhD project highly relevant across multiple academic domains. Although the botany
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200 people, working in four divisions: Algorithms, AI & Data Science, Software, and Interaction. The atmosphere is collegial and informal. You will join the Human-Centred Computing group
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for sustainability in real-life settings; conduct action research and stakeholder engagement; work collaboratively in a team across disciplines and with multiple stakeholders; participate in conferences, workshops and
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Vacancies PhD position on Stochastic geometric numerical methods Key takeaways Are you passionate about developing cutting-edge numerical algorithms at the intersection of geometry, stochastic
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, which has multiple test machines with GPUs and AI accelerators. The algorithms used can be bound by the available compute power or memory bandwidth in different parts of the program. This information will
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specialist collaborator to guarantee adequate integration of perception and action; advanced motion-planning and control algorithms, continuously refined via robotic digital twins, enable reliable handling
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Apply now The Faculty of Science, Leiden Institute of Advanced Computer Science, is looking for a: PhD Candidate, Efficient LLM Algorithm, Hardware and System Design (1.0 FTE) Project description We
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. The research unit Intelligent Systems (IS) in Computer Science is focused on the development of Data Science, Pattern Recognition and Machine Learning algorithms for interdisciplinary data analysis. For more