17 algorithm-development-"Prof"-"Prof"-"Prof" uni jobs at Chalmers University of Technology
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systems. Our work spans the full development cycle, from quantum device design and nanofabrication to low-temperature characterization and quantum measurements. You will join the Quantum Computing Group
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-14156 Is the Job related to staff position within a Research Infrastructure? No Offer Description This position creates an inclusive environment to closely work with the Swedish industry in developing
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Doctoral student in Materials Chemistry of Doped Organic Semiconductors in EU Training Network FADOS
is to achieve targeted modification of semiconductor properties through doping. The goal of the project is to develop fundamental understanding and innovative fabrication processes to solve urgent
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of the previous period, to complete the PhD studies. About SPACER SPACER aims to develop new architectures for porous electrodes to improve the power density and energy efficiency of redox flow batteries (RFB
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of the previous period, to complete the PhD studies. About SPACER SPACER aims to develop new architectures for porous electrodes to improve the power density and energy efficiency of redox flow batteries (RFB
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photonics by developing and implementing experimental methods, maintaining and improving laboratory facilities, and collaborating closely with researchers, PhD students, and external partners. As a Research
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, are required. Important qualities are enthusiasm, to be able to drive and conclude projects independently, take own initiatives and discuss/develop own ideas, be creative and have a problem-solving mindset. The
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description We are looking for a Research Specialist to provide advanced technical and methodological support in integrated photonics by developing and implementing experimental methods, maintaining and
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of AI and materials science, addressing real-world challenges in thermal management for high-performance computing and 6G infrastructure. We offer: (1) You will develop marketable skills in AI, ML, and
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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites