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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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electrolyser devices. However, we do not expect you to have prior knowledge of fabricating and running electrolysers or computational models. You will get the opportunity to learn from both the experienced
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online learning platform with 100+ different courses; 7 weeks birth leave (partner leave) with 100% salary; partly paid parental leave; the possibility to set up a workplace at home; a pension at ABP for
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, conference presentations, and ultimately a PhD dissertation. The PhD thesis is to be completed within four years. You are also requested to teach. You will work with the following supervisory team: - Dr. Frank
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machine learning methods to investigate how ecosystem water stress and drought disturbances affect relevant forest ecosystem functioning at various scales. It will enable advanced assessment of forest
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influence motor system function and learning in healthy people of different ages. This cutting-edge approach will provide new information on the neurophysiological basis of synchronised brain oscillations and
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. This is achieved by working closely together with the different wind farm operators within the Belgian offshore zone, e.g., Parkwind, Norther, Otary. The main focus of the department is on performance
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the Section for Aquaculture, located at our campus in Hirtshals in the beautiful Northern part of Denmark. The section is a vibrant international group that holds a strong expertise in the different aspects
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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trunk around an object. Unlike traditional robots, which rely on rigid links and localized interaction points, soft robots are composed of deformable materials that can conform to complex geometries and