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or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience in interdisciplinary collaboration
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journals or conferences; experience with or a strong aptitude for using advanced computational tools, AI, or machine learning techniques to address engineering challenges; and interest or initial experience
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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mechanical loading of such samples. The focus of the PhD project will be to use machine learning techniques to better understand the interplay between the crystal orientations and deformation patterns in a
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students who are prepared for a lifetime of learning and rewarding work. Candidates should hold a PhD or master’s degree in electrical and computer engineering or related fields and should be comfortable
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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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. Qualifications: Required Education and/or Experience: Must have a PhD degree from an accredited institution of higher learning; or Must have a Master's degree from an accredited institution of higher learning and
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flexibility orchestration Scalable data and machine learning pipelines Digital twin architectures for cyber-physical energy systems AI-based energy system modeling, simulation, and optimization Secure and
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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FAIR principles, combined with skills in statistical analysis, machine learning and/or data science. Experience with programming languages such as R, Python, or similar will be considered an advantage