51 phd-agent-based-modelling positions at Chalmers University of Technology in Sweden
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for a PhD student focusing on future fuels and propulsion systems for the maritime sector. This is an opportunity for you to contribute to the development of techno-economic and environmental assessment
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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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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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to emerging digital technologies Interplay between technology development and business model evolution - how advancements in technologies reshape value creation and value capture, necessitating continous
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the models and measurement methods. The position is based at the Division of Transport, Energy and Environment within the Department of Mechanics and Maritime Sciences . The division conducts
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of Chemistry and Chemical Engineering department at Chalmers. We have a wide range of experience and work both experimentally and with modeling. We have a long tradition of working with both fundamental
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municipal spatial planning to support the transition from a linear to a circular built environment, and to shift from new construction to transformation and renovation. The project adopts a practice-based
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nitride (hBN)-based thermal interface material in this project. The hBN material offers high thermal transfer performance while providing electrical insulation for AI booted electronics and battery cooling
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PhD students in the Automation group, with the primary goal of qualifying for a future academic career. Contribute to research projects within discrete-event systems, supervisory control theory, and