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fascinated by the possibilities of employing neural networks in computer vision? Would you like to develop methods with the potential to address critical societal challenges? Would you thrive in an innovation
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the research community at conferences and workshops. You should bring the following qualifications: A PhD degree in (computational) neuroscience, biomedical engineering, computer vision, machine learning
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against the responsibilities and qualifications stated above and the following general criteria: Experience and quality of teaching Research experience, vision and potential International impact including
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-based power system. We expect that you will build on a deep scientific foundation within power engineering, automation and control, computer science and/or similar discipline(s). As a professor at DTU you
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general criteria: Experience and quality of teaching Research experience Research vision and potential International impact and experience Societal impact Innovativeness, including commercialization and
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, extraction of biomolecules, and preparation of samples for sequencing. Knowledge of computational pipelines for processing of datasets and collaborating with colleagues for advanced bioinformatic analysis
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Deadline 24 Jun 2024 - 12:00 (Europe/Copenhagen) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? H2020 / ERC Is the Job related to staff
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using advanced operando electron microscopy at the Center for Visualizing Catalytic Processes (VISION). PhD Scholarships in Applied Mathematics – DTU Compute Kgs. Lyngby, Denmark Posted on 05/16/2024 Two
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nanoparticle catalysis using advanced operando electron microscopy at the Center for Visualizing Catalytic Processes (VISION). PhD scholarship in Post-Quantum Cryptography - DTU Compute Kgs. Lyngby, Denmark
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for Bioinformatics at DTU Health Tech In the Section for Bioinformatics, we combine knowledge of biology with informatics to study questions that are often relevant to human health but equally often of a fundamental