218 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in Denmark
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, laboratory automation (robotics), molecular biology, analytical chemistry, data science, and food science. The REFINES project is funded by the Innovation Foundation’s Grand Solutions program and is a
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industrial and academic partners. The overall goal of the project is to optimize the design of water eletrolyzers for efficient green energy production. You will be conducting Computational Fluid Dynamic (CFD
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levels, ranging from BSc, MSc, PhD to lifelong learning students. We have about 300 dedicated employees. Read more about us at www.energy.dtu.dk. Technology for people DTU develops technology for people
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positive and supportive team environment. You prefer to stay organized and take pride in completing your tasks in a thoughtful and structured way. As a formal qualification, you must hold a PhD degree (or
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characterization (e.g. NMR), biofilm extracellular polymeric substances, biophysics, glycoconjugates, complex assembly, or lectins, is an advantage. Your profile Applicants should hold a PhD in glycobiology
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will report to the Head of Department. In your daily job, you will report to Associate Professor Lasse Sommer Kristensen. Your competences You have academic qualifications at PhD level, preferably within
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of port electrification with energy storage, Power-to-X(PtX) solutions, and grid-supportive services. Qualifications The successful candidate must hold a PhD in Electrical Engineering, Power Electronics
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of scientific articles. Teaching is not required as part of this position. Daily work will be conducted in English, and familiarity with Danish is not necessary. Required qualifications PhD in pertinent field
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collaborate with PhD and other post doctoral fellows, in cross-disciplinary collaborations with other research groups both national and international as well as with industrial partners. Qualifications A PhD
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Natural Language Processing, Machine Learning, or a similar area. Expertise in large language model architectures and training paradigms (transformer models, fine-tuning strategies, RLHF, etc.). Interest in