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Field
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integrated circuit design, testing and simulation using Cadence is a plus Knowledge of evolutionary optimization methods is a plus Very good communication skills in English, both spoken and written. PGI-15
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sciences, life sciences or psychology Language requirements Applicants have to provide proof of proficiency in English through the TOEFL, IELTS or an equivalent test. English native speakers do not need
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family-friendly university. We welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant
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test, ergonomics advice at the workplace Team: AG Landthaler Publication period: 08.09.2025 - 08.10.2025 Desired starting date: 01.01.2026 Job location: MDC Berlin-Mitte Hannoversche Str. 28 10115 Berlin
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Your Job: The department of Electrocatalytic Interface Engineering, headed by Prof. Dr.-Ing. Simon Thiele, focuses on the development, characterization and testing of electrochemical systems from
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 1 month ago
@ipk-gatersleben.de ) Please indicate the reference number 54/08/25 in your correspondence. Foreign qualifications must carry out an equivalence test in Germany, which is subject to a fee. This must be
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using Neural ODEs, which inherently follows the principle of mass balance, but have more flexibility in matching the complex rainfall-runoff relation. To validate and test the proposed hybrid framework
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on the campus of Forschungszentrum Jülich. Your tasks include in detail: Construction and commissioning of a new test stand for the investigation of the continuous butanediol dehydrogenation under dynamic
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acids Binding efficiency, inhibition and selectivity tests Structure-based analysis of polyphillic interactions Presentation and publication of scientific data The opportunity to obtain your own academic
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them into MATSim. It will involves building realistic test scenarios, running simulations, and progressively refining agent learning strategies using techniques like curriculum learning and reward