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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed
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(neutral and doped quantum dots in micropillars, NV centers in waveguides, optical nonlinearities, etc.). Approaches may be analytical, based on the collision model, or numerical. The postdoctoral researcher
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of numerical optimization is an advantage. Experience from high-performance computing is an advantage. Applicants must be able to work independently and in a structured manner and demonstrate good collaborative
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to perform research at the energy-climate interface that expands our understanding of climate impacts in energy systems. The work will combine ensembles of climate and weather data with numerical modeling
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-accurate-portable-diagnostics/. What you’ll do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries
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renewable energy. Responsibilities include calibrating simulations with experimental/numerical data, performance analysis, and contributing to interdisciplinary research on coastal protection, with
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with numerical modeling, energy system optimization and possibly machine learning to guide energy transitions towards net-zero systems. The research supervisors have prepared multiple potential projects
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do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries to improve sensor performance
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printing. Plan and carry out experimental programs involving the fabrication, testing, and characterization of concrete and composite materials. Develop architected structures using numerical simulation
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optimization Signal processing and general communications background Programming skills in MATLAB and/or PythonGood writing and oral presentation skills, in English Eligibility requirements of MSCA: Research