20 neuroimaging-machine-learning-engineer PhD positions at Chalmers University of Technology in Sweden
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of the PhD student will touch upon various topics multi-body dynamics, optimal control theory, machine learning and robotics and artificial intelligence in general. The focus is broadly upon the development
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Are you curious about harnessing the unique flavour properties of micro- and macroalgae? In this project at Chalmers University of Technology , challenges and possibilities with algae as food
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to optimally prepare it for use in AI-driven systems and solutions. About us The Department of Computer Science and Engineering at Chalmers and University of Gothenburg in Sweden with approximately 300
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at the Department of Microtechnology and Nanoscience at Chalmers University of Technology in Gothenburg, Sweden. Project description The continuous miniaturization of silicon-based electronics has driven
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: The successful candidate must have a Master's degree in electrical engineering, engineering physics, or related disciplines. Completed courses in signal processing, radar or communication theory are meritorious
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) Qualifications Required Undergraduate degree in Civil Engineering or a related topic, with strong emphasis on Geomechanics Fluency in spoken and written English Willingness to learn Swedish, as necessary
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modelling at both catchment and road scales, incorporating input from relevant stakeholders.The candidate will gain extensive knowledge in hydrological modelling, climate adaptation, road engineering, and
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senior researchers, three postdocs and three PhD students. It is embedded in an interdisciplinary environment where we have close collaboration with other research teams at Chalmers such as technology
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. Project overview The position will be located at the Department of Chemistry and Chemical Engineering in the Bordes Research Group, which has a long experience of research on surfactant. In recent years
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these systems operate in, ACPS increasingly rely on data-driven learning-enabled components to perform a variety of challenging decision-making tasks. While indispensable for autonomy, learning-enabled components