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Experience in deep learning/generative AI or molecular modelling Prior research or industrial exposure Ability to work in a multidisciplinary and collaborative environment How to apply: The application
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/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
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technology, building physics, HVAC systems, computer science and control systems architecture, thereby advancing all disciplines involved. The project is a collaboration with Building Services Engineering
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Wiberg is “Innovative statistical and machine learning methods for comparing performance and outcome in register data studies”, with overall aim to develop, evaluate, and implement innovative statistical
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on forests and forestry as complex socio-ecological systems. We closely collaborate with multiple stakeholders and conduct applied research in silviculture, forest ecology, pathology, policy and planning. We
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies
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computational drug metabolism project in collaboration with AstraZeneca and Chalmers University of Technology, funded through the Wallenberg National Program for Data-Driven Life Science (DDLS). Chalmers
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. We care about creating a positive, respectful, and stimulating environment, valuing communication and collaboration and a workplace that promotes learning and development for all. We are committed
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Chalmers, you are an employee and enjoy all employee benefits. Read more about working at Chalmers and our benefits for employees. The position is limited to four years, with the possibility to teach up
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systems, complex networks, data-driven modeling, machine learning, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the