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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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employed at Lund University, Work with u s. Work duties In the role as Project Assistant you will use machine learning and other advanced statistical techniques to develop precision prediction models using
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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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++, Fortran) Background in mechanics of materials or computational modelling Experience with machine learning for physical fields/PDEs/GNNs and HPC workflows Interest in interdisciplinary collaboration and
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with organisational learning and support industry’s transition toward more resilient and sustainable manufacturing. The work is carried out in collaboration with projects in assembly design, digital
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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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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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computational methodologies, ranging from atomistic and electronic-structure–based materials modeling and characterization, via machine-learning and high-throughput methods, to ab initio calculation
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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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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