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teaching courses and co-supervision of BSc and MSc. Qualifications MSc graduates with a background in either engineering, mathematics, computer science, computer engineering, physics, sustainable energy
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Networks context. You are a committed individual with a strong background in engineering or applied mathematics / computer science, with a keen interest in scientific programming, machine learning and data
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background in engineering, with a keen interest in scientific programming, machine learning and reliability engineering. Your curiosity drives you to explore and understand the intricacies of wind energy
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experience within the field of optimization and machine learning You must also fulfill the requirements for admission to a PhD program at DTU. You must have a two-year master's degree (120 ECTS points) or a
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science, machine learning and other methods. Within the section, you will be working within a diverse team of scientists at multiple levels of their career. Approval and Enrolment The scholarship
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, the methods employed within the section include, system engineering, optimization methods, multi-disciplinary design optimization, uncertainty quantification, data science, machine learning and other methods
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-Computer Interaction IT Governance, Architecture and Compliance Metaverse and Augmented/Virtual Reality (AR/VR) in Business Neuroscience and Digital Behavior Quantum Computing Robotics and Smart