26 parallel-computing-numerical-methods uni jobs at Chalmers University of Technology
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Engineers within the international master’s programme Mobility Engineering. About the research project This project aims to improve ship energy performance through the integration of AI, big data analysis
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staff position within a Research Infrastructure? No Offer Description Join our computational mechanics team at Chalmers University of Technology ! As a Doctoral student with us, you will develop numerical
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the lab, you will be exposed to a broad range of computational methodologies, ranging from material characterization, via machine-learning and high-throughput methods, to ab initio calculation
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actions to evaluate, balancing safety and computational effort. You will compare deep learning–based methods and probabilistic machine learning approaches, and explore extensions to active reachability
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communication with the Swedish marine industry will take place on a regular basis. Main responsibilities As a full-time doctoral student, you will be expected to independently develop numerical methods and codes
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methods for creating innovative heat-exchanger geometries with high efficiency and sustainability in mind. You will use open-source machine learning software provided by Meta AI and OpenAI, deployed
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Research Area at the Department of Architecture and Civil Engineering . This research area focuses on developing methods, tools, and strategies to enhance the sustainability performance of the built
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statistical clustering methods to develop a typology of interface conditions. The doctoral student will also lead an empirical study assessing how different interface types affect street liveliness and
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(R2) Country Sweden Application Deadline 14 Nov 2025 - 22:00 (UTC) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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given the opportunity to work in one of the world-leading groups on blue-UV surface-emitting lasers and combine device design and numerical simulations with device processing and characterization in close