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group specializes in experimental quantum technologies spanning quantum information processing, quantum communication, and precision sensing. In this PhD project, you will work at the forefront of solid
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recent large-scale capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning. Explainability, leveraging identifiability and unique recovery
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intelligence (AI) to optimize large dataset analysis, improving decision-making processes for awarding grants to firms and scientists. Key research areas include: Enhancing Selection Decisions: How can we make
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similar degree with an academic level equivalent to a two-year master's degree. You are highly motivated to help solve the global energy problem and you want to work in a big, diverse research project
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collaborations. Practical experience with waste materials, and experience in laboratory work related to microplastics and/or chemical risk assessment is a large plus. A proactive and independent work style. A
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code, combined with advances in automation, analytics and data science, has fundamentally changed the scope and ambition of harnessing the potential of biological systems. Big data approaches and
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof
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This project explores the use of state-space models (SSMs) for sequence modeling in large language models (LLMs), focusing on their CMOS-based hardware implementation. The PhD candidate will develop energy