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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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tasks will be to: Genetic engineering of bacteria. Phenotypic characterisation of engineered strains. Teach and supervise BSc and MSc student projects. You must have a two-year master's degree (120 ECTS
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computationally efficient numerical structural models. To support the condition (state) assessment, the project will also explore the use of advanced estimators (e.g., Kalman Filter) or Machine Learning models
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background in CMOS/VLSI design, computer architectures (preferred RISC-V architecture), and deep learning principles. Experience with industry-standard EDA tools such as Cadence suite: Genus, Virtuoso, Spectre
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, fluid-structure interaction) Desire to develop interdisciplinary expertise across hydrodynamics and structural mechanics. Experience with or willingness to learn: Programming (e.g. C++, Python, Matlab
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Policy Implications and Recommendations Case Studies of Successful Innovation Funding Methods The project will employ a combination of methods, including machine learning (ML) and generative AI (GenAI
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results. The ability to work collaboratively in a team with an open-minded spirit, embracing both teaching and learning opportunities. A genuine interest in discussing physics and engaging in thoughtful
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to acquire greater knowledge about basic scientific problems and to conduct research oriented towards use in societies and companies. Technology for people DTU develops technology for people. With our
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scholarship, to contribute to the project’s substantial collective prosopographical work (among other things with the aim of creating a new version of VisuaLeague ), and to learn and grow as a scholar together
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electronic components including SiC/GaN based devices. The candidate will also have opportunity to work with industry related problems. Key Responsibilities Research & Learning: Develop expertise in advanced