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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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simulation, energy efficiency, and tool development. We offer interesting and challenging tasks at the forefront of research in a flexible work environment. The tasks include supporting 3D modelling in Rhino
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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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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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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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developing a novel imaging and amperometry-based platform for research into neurological diseases. About us The Esbjörner lab belongs to the Division of Chemical Biology , which is part of the Department
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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