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quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research on an international stage To apply, please submit a complete CV, letter of motivation, university
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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diverse working day is guaranteed! During the project, you will develop and implement self-learning control algorithms that balance computational demand and modeling precision. You will evaluate, interpret
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diverse working day is guaranteed! During the project, you will develop and implement self-learning control algorithms that balance computational demand and modeling precision. You will evaluate, interpret
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck of computational load for such a development. In the frame of a
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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) and the University of California Irvine (UCI). The Research School "Foundations of AI" focuses on advancing AI methods, including energy-efficient and privacy-aware algorithms, fair and explainable