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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
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Join us for your PhD journey – help us explore the future of advanced Human-Centred Production in Industry version 5.0. The Industry of Tomorrow relies on constantly upskilled people in a future
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disciplines learn together. Our team is diverse and comprises researchers and teachers with architectural, planning, urbanism, and human geography backgrounds, Doctoral students, and other early-career
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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about working at Chalmers and our benefits for employees. The position is limited to four years, with the possibility to teach up to 20%, which extends the position to five years. Doctoral studies
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aimed at building a high-performance quantum computer based on superconducting circuits. Our team includes a dynamic mix of PhD students, postdocs, and senior researchers working collaboratively
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: For this position it is required to have a PhD in Catalysis. To qualify for the position of postdoc, you must hold a doctoral degree awarded no more than three years prior to the application deadline. * It is a merit
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the sustainability performance of the built environment. Job description We are looking for three student assistants to support the PhD project Measuring to Manage: A Methodological Framework for Quantifying Plastic
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, improving scripts in Grasshopper, running energy simulations and supporting literature reviews, amongst others. You will be guided by an experienced postdoc in the group and collaborate with different PhD