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of digital environments utilizing real-time data for dynamic risk evaluation; (3) the advancement of risk-to-resilience methodologies; and (4) the establishment of digital twin-based resilience frameworks
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-performing, and collaborative PhD student to join our dynamic and international research group at Mid Sweden University, comprising over 30 researchers from around the world, all passionate about innovation
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) the development of a holistic multi-hazard risk framework capturing cascading effects across systems and scales; (2) the creation of digital environments utilizing real-time data for dynamic risk evaluation; (3
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Job Description You will join a supportive and dynamic research team working at the intersection of machine learning and operations research. Your main task will be to design and implement ML
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, or similar. Familiarity with linear algebra libraries and high-performance computing is a merit, but not a requirement. About the position The position provides you with the opportunity to pursue PhD studies
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materials (natural and synthetic fibers, yarns and fabrics) which are highly anisotropic and non-linear. Furthermore, the dynamics of high-speed manufacturing processes need to be included in the modelling
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expected date of completion. We recognize that educational timelines vary and welcome applicants who have followed non-linear academic paths. Copy of your (Research) Master’s thesis: If the thesis is not in
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Principal Investigator (PI) or Co-Principal Investigator (Co-PI) on research studies. Perform non-linear, dynamic, finite element analysis (FEA) and design for various research studies involving low- to high
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prediction, signal tracking, fluid dynamics, and space exploration. Advancing Signal Modelling with Physics-Informed Neural Networks This project aims to develop Physics Informed Neural Networks (PINNs
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What if you could design systems that not only follow instructions — but understand intent and guarantee correct behavior over time? We are looking for up to two PhD students who want to explore