10 computational-materials-science PhD positions at Chalmers University of Technology
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Are you interested in developing computational tools to understand the detailed mechanical behaviour of multi-phase materials? Then this PhD position at Chalmers University of Technology might be
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AIPhD in computational modelling RAM³ is a WASP-WISE NEST project This recruitment is connected to the Wallenberg Initiative Materials Science for Sustainability All early-stage researchers recruited
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NEST project RAM³, which aims to enable the use of recycled aluminium in high-performance applications through machine learning, computer vision, and materials science. The focus of this position is on
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, Sweden. The student will form a part of a new NEST initiative funded by the Wallenberg Initiative Materials Science for Sustainability (WISE) and the Wallenberg AI, Autonomous Systems and Software Program
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machine learning, computer vision, and materials science. The focus of this position is on development of neuro-symbolic models for the effective behaviour of the complex microstructure of recycled
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cladding materials. We will develop a full methodology for computational design, robotic 3D printing, assembly and disassembly of the panels, and demonstrate their application in typical insulated wall frame
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Physics, Chemistry, or Materials Science. A strong background in quantum mechanics, thermodynamics, and statistical mechanics. Documented experience in programming. A strong interest in atomistic modeling
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The Department of Architecture and Civil Engineering (ACE) at Chalmers University of Technology has approximately 250 employees, encompassing a broad theoretical and practical knowledge base. In ACE, the Division
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of Industrial and Materials Science (IMS) . The Maintenance Engineering research group is a vibrant, ambitious, and multi-disciplinary team of international researchers driven by the vision to renew
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. The applicant should have strong background in mathematical foundations of computer science and experience in Python programming. Previous experience in deep learning, reinforcement learning, or explainable AI is