64 postdoc-in-cognitive-radio PhD positions at Technical University of Denmark in Denmark
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of increasingly renewable energy systems. The ideal candidate will have experience or an interest in the following areas: Knowledge of computer science and machine learning. Familiarity with electrical and
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to current literature and published in conference and journal articles. You are required to have an excellent academic background and must have a strong knowledge of production engineering technologies and
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Microscopy (SEM, AFM), Spectroscopy (e.g. FTIR, Raman), and antimicrobial assays, and/or other relevant methods for characterizing packaging materials and foods. Previous knowledge of LCA is also a plus
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or plasmid stabilization with different selection markers will be pursued. Responsibilities As a PhD candidate, you will contribute to increasing our knowledge and molecular tools for production of proteins
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research, educational activities, and innovation at a high international level within e.g. energy, catalysis, and materials. The overall aim is to contribute with new knowledge about basic scientific
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to acquire greater knowledge about basic scientific problems and to conduct research oriented towards use in societies and companies. Technology for people DTU develops technology for people. With our
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joint projects Co-supervise BSc and MSc student projects and contribute to the teaching at our institute Present research results at international conferences and workshops Engage in knowledge transfer
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on generating new knowledge for optimizing biological conversion of carbon dioxide to acetic acid in close collaboration with an industrial end-user of the developed technology. Responsibilities and tasks Your
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learning, or reinforcement learning) Have experience developing algorithms for combinatorial optimization problems Have knowledge about decomposition methods for mixed integer linear programs Are motivated
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and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited. The project will be executed in three