53 professor-computer-science PhD positions at Technical University of Denmark in Denmark
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, geophysics, materials science, computation, engineering, or a related discipline. Hands-on research experience in one or more of the following areas will be considered an advantage: Confocal microscopy and
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effective enzymatic recycling processes. We are looking for candidates with strong qualifications in some of the following areas, and a motivation to develop within others: Protein chemistry Enzyme kinetics
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Job Description If you are looking for an exciting opportunity for PhD, we at Materials and Surface Engineering section, Department of Civil and Mechanical Engineering, Technical University
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DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and
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. The project will be supervised by Associate Professor Martin Hansen (DTU Sustain), and co-supervised by Professor Hans Peter Arp (NTNU Chemistry), Senior Scientist Anna Rosenmai (DTU Food), Veerle Jaspers (NTNU
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, including electrical engineering, control theory, industrial engineering, electronics engineering, energy policy, data science, and applied mathematics. As part of the Alliance program, your project will be
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degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree in microbiology, biology, veterinary science, food science, or a related field. Approval and
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such as CAPeX. You are an experimentalist - second to none. You have experience in one or more of the following areas: Analytical electrochemistry Vacuum science/surface science MEMS chip design and
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will, in addition, sharpen your didactics skills through experience as a teaching assistant. You must hold a two-year master's degree (120 ECTS points) in Robotics, Electrical Engineering, Computer Science
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon