93 formal-verification-computer-science PhD positions at Technical University of Denmark in Denmark
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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
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with methodologies such as AI-assisted evidence synthesis and quantitative health impact assessment and become part of an interdisciplinary research environment with strong links to DTU Compute and the
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, might be for you! Responsibilities and qualifications Working with colleagues in the MULTIBIOMINE project, you will develop computational methods that use novel strategies to uncover hidden features in
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of the research group. Desired qualifications and skills: A relevant background in aquatic biology, animal physiology or a related field. Good skills for laboratory-based analytical tools. Practical experience
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Job Description Are you passionate about sustainable innovation, food safety, and creating real-world impact through cutting-edge materials science? Do you want to help design the future of food
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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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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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, 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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group and are expected to contribute to other departmental tasks. We expect that you have a background in biology or veterinary medicine and have an interest in interactions between diet, intestinal
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deformation. Responsibilities Develop scientific machine learning methods in close collaboration with team members specializing in experimental techniques and materials science. Utilize unique experimental data