32 applied-harmonic-analysis PhD positions at Chalmers University of Technology in Sweden
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experience that will strengthen your application: Operating filters or reactors Molecular biology techniques Programming, data analysis, and statistics Mathematical modelling Wastewater engineering Contract
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Proficiency in molecular biology techniques Skills in data analysis, statistics, or programming Background in wastewater engineering Contract terms The position is full-time for 4 years, with the possibility
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We are looking for a highly motivated, skilled, and persistent PhD student with experience in computational fluid dynamics (CFD) and some knowledge in structural analysis. The research aims
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technology, or applied mathematics, with a specialization in energy systems analysis, or energy economics. Strong written and verbal communication skills in English. The following experience will strengthen
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housing renovation. The aim is to identify ways to bring together the shared goals of property owners, society and tenants – where reuse and resource efficiency in renovation can harmonize with
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expected to work across the full pipeline, from sensor-based measurements to data analysis, supporting a holistic approach to personalized rehabilitation. In addition to fall risk assessment, the project may
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under the guidance of academic supervisors Apply and further develop tools and methods for risk assessment, such as multi-criteria analysis and cost-benefit analysis Collect and analyse empirical data and
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Biomedical Engineering conducts leading research in image analysis, computer vision, and machine learning, with a growing emphasis on generative AI and AI for scientific discovery. Our mission is to develop
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Are you interested in developing computational tools to understand the detailed mechanical behaviour of multi-phase materials? Then this PhD position at Chalmers University of Technology might be
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effort at the intersection of machine learning and applied mechanics. The focus of this position is on extracting information about what a neural network has learnt in a symbolic and (human) interpretable