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directions will be pursued to enhance column generation using machine learning. The first line of research focuses on improving scalability by using Graph Neural Networks to identify and eliminate non
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including interpersonal skills, teamwork and networking ability, and presentation skills Strong personal competence including autonomy, goal-oriented mind-set, planning/organizing/prioritizing, perseverance
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PostDoc in the project). Collaborating with fellow researchers across the UPLIFT network, including those focused on digital twins at DTU Chemical Engineering- As a PhD candidate, your work will adapt
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, communication, networking, presentations, writing of manuscripts etc. Mentoring and career development in a network of national and international collaborators Research exchange to collaborating institutions
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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individualized course program Training in a wide range of skills including research management, communication, networking, presentations, writing of manuscripts etc. Research exchange to collaborating institution
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. Responsibilities: Conduct research in time-predictable computer architecture. Designing a network-on-chip for real-time automotive systems Verify the design with modern verification methods, such as function
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, and teamwork. You will have a great opportunity to build strong networks with internationally known researchers at DTU and other universities as well as leading industrial partners. As a PhD candidate
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research environment with a strong scientific and social network for students and postdocs and good mentoring opportunities to support individual career development. You will have access to state-of-the-art
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collegial respect and academic freedom tempered by responsibility. We develop talent by offering national/European and global networking opportunities, unique research infrastructures, and a collaborative