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Field
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approach is a method that is both formally rigorous and practically efficient for the verification/validation of quantum devices. Candidate’s profile Knowledge of quantum computing and an understanding
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, such as household decision-making, willingness to invest, adoption of renewable energy technologies, and social acceptance of innovations. The goal of the PhD is to develop validation methods, best
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. Empa is a research institution of the ETH Domain. The Urban Energy Systems Laboratory (UESL) pioneers strategies, solutions, and methods to support the development of sustainable, resilient, and
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. For more information, please visit our website: www.uni.lu/snt-en/research-groups/finatrax/ The selected candidate will be enrolled in the PhD program in Computer Science and Computer Engineering with
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, Sociology or related fields). The data collection is to take place in Denmark, so the PhD student must be fluent in Danish. Experience with qualitative methods is required, and related research experience
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-intensive PhD training programme, supported by the PRIDE funding scheme of the Luxembourg National Research Fund (FNR) and the programme's partner institutions: University of Luxembourg, Luxembourg Institute
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organisation Internal Evaluation Procedures: The research school will organise a yearly formal meeting with a thesis advisory committee (TAC), where each PhD student will give an oral report on the progress
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into this material and support tailoring its properties. For this, you will: Contribute to method development for ultra-fast MLIPs (Xie et al., npj Comput. Mater., 2023) Develop realistic MD simulation protocols
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, or willingness to work with them Experience with multi-modal machine learning methods Familiarity with formal linguistics, particularly formal semantics and pragmatics We encourage applications from individuals
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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided