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The Faculty of Electrical Engineering and Computer Science, Leibniz University Hannover | Hannover, Niedersachsen | Germany | 20 days ago
development of intelligent systems capable of learning from their environment, interacting, and solving complex problems independently. Close cooperation with the institutes of the faculty as well as an active
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socio-economic analysis, equipping them with advanced skills in reservoir modelling, machine learning, advanced oxidation processes, and microbial enhanced recovery. Candidates will also develop intuitive
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, artificial intelligence (AI), and socio-economic analysis. This program will equip them with advanced expertise in reservoir modelling, machine learning, advanced oxidation processes (AOP), and microbial
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physiology is an asset Experience in statistics and data analysis of large data sets (including time series analyses and machine learning) Good programming skills Very good communication and interpersonal
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Solid knowledge and experience with CAD (Computer-Aided Design) programs and the creation of technical drawings Fluency in spoken and written German (min. CEF Level C1) Fluency in spoken and written
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from the date of defense of your PhD and until September 9, 2026. Career breaks (parental leave, military service, work outside research, etc.) do not count towards the amount of research experience must
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part of the Forschungsverbund Berlin (https://www.fv-berlin.de/ ) and the Leibniz Association (https://www.leibniz-gemeinschaft.de ). You can find more details on the institute webpage: https://www.ikz
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development of intelligent systems capable of learning from their environment, interacting, and solving complex problems independently. Close cooperation with the institutes of the faculty as well as an active
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research with the combined tools of immunology, microbiology, virology, cell biology and molecular biology. For more information, please see https://www.mhh.de/hbrs/zib MD/PhD Molecular Medicine
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behavior on crystal defects. Perform active research on the materials synthesis, characterization, and device fabrication. Receive individual trainings to learn state-of-the-art methodology, comprising