42 data-mining-phd Postdoctoral positions at Chalmers University of Technology in Sweden
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will be run in the Division of Computer Networks and Systems within the Department of Computer Science and Engineering . Within the Division we have four faculty members, four postdocs and eleven PhD
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stiffness or stretchability. You will collaborate closely with PhD students and Postdocs in our group as well as external partners to study the mechanical, electrical, and electrochemical properties
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international network and the possibility to make a strong impact through your research. Project and role description The research focuses on combining information from different modalities (e.g., radio, radar
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Physics Division that hosts the Langhammer Group provides further relevant information. Who we are looking for We seek candidates with the following qualifications: A PhD degree in physics. An experimental
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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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of the Wallenberg Centre for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing
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project Participate in teaching activities Qualifications You have a PhD degree in a field related to High Performance Computing or Computer Architecture. You are highly motivated, self-propelled, energetic
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datasets. By integrating data-driven insights with innovative modeling, this interdisciplinary project aims to enhance our understanding of vulnerability, resilience, and adaptation, ultimately informing
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The fully funded 2-year Postdoc project "Modelling of thermomechanical behaviour of AM-built microstructures of superalloys based on data from large-scale neutron and synchrotron infrastructures
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applications, specifically targeting the prognosis and risk prediction of Heart Failure (HF) in patients. This research integrates AI safety, explainability, and multimodal medical data analysis to enhance