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Job Description If you are ready to launch your research career in advanced manufacturing and want to build cutting-edge skills in AI and real-time data-driven production, this PhD opportunity is
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Applicants should hold a relevant MSc degree in electronics, electrical engineering, computer engineering, or related fields. Required Qualification: Solid background in digital CMOS design and deep learning
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of the general degree programmes at DTU. 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
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cell and stack using experimental data and advanced modeling techniques. Propose and implement improved operation modes to minimize degradation effects and enhance performance. As part of the PhD
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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conceptual framework linking nanoscale features to macroscopic adsorption efficiency. Generate and curate high-quality datasets to support data-driven materials optimization and future integration with AI
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analyse your data You will be coordinating the collaboration with CCIT, which involves retrieving samples and clinical data from a team of doctors. You will have to present in lab and section meetings
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components are in use. More specifically, the PhD position will look towards connecting different advanced software tools (of multi-physics and data-based models) simulating the metal AM process
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master's degree. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information