55 algorithm-development-"Multiple"-"Simons-Foundation" positions at Chalmers University of Technology
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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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on-wafer characterization of high-frequency transistor devices, including bias-dependent S-parameters, pulsed I/V, and load-pull measurements Develop empirical device models that capture static, dynamic and
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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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pioneering research on rethinking space and municipal planning processes conducted in co-creation and a transdisciplinary setting spanning architecture, urban development, human geography, ecology, and economy
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project focuses on developing and refining advanced organ-on-chip technologies to study and optimize nanoparticle-based delivery systems in biologically relevant environments. The project is strongly
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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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We are looking for a postdoc to join a project on carbon capture and utilization (CCU) at Chalmers. In this role, you will focus on developing catalysts and carrying out detailed characterization
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to the development of advanced methods for electrode fabrication, with a particular emphasis on electrophoretic deposition. You will also conduct electrochemical testing under applied magnetic fields to improve
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this role, you will focus on developing catalysts and carrying out detailed characterization experiments that can support the future production of electrofuels and electrochemicals. You will be part of a
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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