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and exact optimization methods enhanced by machine learning (ML). The overarching goal is to solve large-scale combinatorial optimization problems more efficiently, particularly in domains such as
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prior experience in at least three of the following areas: Python programming Develop LLM-based tools to automate data connector generation for data ingestion. Design and implement a multi-layered storage
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English and interpersonal skills for working in a multi-disciplinary team environment. Willingness to engage in group work with a multi-national team; Able to work independently; Approval and Enrolment
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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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‑band acoustic systems, to extend DTU Aqua’s ocean‑observation capabilities. Process and analyse multi‑sensor data to characterise marine fish communities and their habitats. Contribute
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experimentation with Asst. Prof. Eli N. Weinstein. Your goal will be to develop fundamental algorithmic techniques to overcome critical bottlenecks on data scale and quality, enabling scientists to gather vastly
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of grading scale You may apply prior to obtaining your master's degree but cannot begin before having received it. Applications received after the deadline will not be considered. All interested candidates
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to capture vPvM chemicals in water. Optimize effect-directed analysis and implement suitable in vitro assays Investigate operational waterworks and if possible test pilot-scale systems such as advanced
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include: A letter motivating the application (cover letter) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale You may apply prior
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. The group also contributes to research-based advice and activities associated with updating the Danish Food Composition Database. The group has a dynamic staff with high international visibility, many