219 machine-learning "https:" "https:" "https:" "https:" "https:" positions in Sweden
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for molecular dynamics (MD), slashing computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with
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allocation proposals, conducting machine learning workflows, and developing complete models. Example applications include microscopy image data, cryo-electron microscopy, structural prediction and dynamic
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machine learning Experience in analyzing plant community, insect community and/or vegetation data Driving license (car) Consideration will also be given to good collaborative skills, drive and independence
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group, which specializes in machine learning and systems glycobiology, and will be financed by the Future Research Leaders 9 (FFL-9) program from the Swedish Foundation for Strategic Research. Doctoral
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested in developing new image analysis and machine learning methods
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industrial partners experience of scientific publishing in machine learning and artificial intelligence. Merits: proficiency in programming languages such as Python experience in data analysis and
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/NIR) for separation and material sorting, and use machine learning for process optimisation and performance prediction from fiber to finished product. Functional processing of recycled materials and AI
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are important. You are welcome to apply for a job with us! Here you can learn more about what it's like to work at Malmö University: Work with us We are looking for Research assistant in Machine Learning with a
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at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. Project description The focus of the project is unconventional computing, and, specifically, the development
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Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg | Sweden | 3 months ago
mechanisms in normal neural development (demonstrated by us and colleagues) and may harbor cues for novel treatment strategies. Omics data can be used in black box machine learning algorithms to classify or