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of distributed systems and networks for machine learning inference. Applying machine learning concepts, with the goal of devising agentic frameworks, will also be a part of the project. Supervision: Professor
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researchers develop new machine learning (ML) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division , our group
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is also on-going in the subjects of Process Metallurgy, Cyber-Physical Systems, Experimental Mechanics, Machine Learning, and Operations & Maintenance, to enable RECAT’s ambitious goals. Subject
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mature commercial solvers are known to sometimes produce wrong results. Our work on designing a new generation of certifying combinatorial solvers, which output not only a solution but also a machine
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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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/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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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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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation