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within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in
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long-term, and most often global, perspectives on future renewable fuels for transport. We seek to rigorously analyse the feasibility of energy transitions, utilize empirical as well as estimated data
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computer. The WACQT team at Chalmers currently has about 100 members (faculty, permanent research staff, postdoctoral researchers, PhD students, and undergraduate students). WACQT is committed to promoting
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the division of Geoscience and Remote Sensing we conduct research to tackle global environmental problems and to understand processes of the Earth system. We develop sensors, gather and analyse data
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collaborative manner. Conducting interviews, workshops, and collecting case study data, translating the case observations into analytical representations, writing and presenting high quality papers, and being
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), recovery of critical raw materials, and the synthesis of new materials from secondary sources. More information about the research can be found here: Industrial Materials Recycling – Chalmers Main
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scientific articles, reports, and presentations at conferences. Collaborating with researchers and stakeholders to collect data, discuss results, and organize workshops. Completing coursework required
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construction. Information about the research environment The PhD student will join the research environment Architecture, Media, and Material Practice (AMMP) at the Department of Architecture and Civil
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general information about doctoral studies at Chalmers here Application procedure The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please