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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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PhD Position - Organic Electrosynthesis: monitoring of reaction transients with real-time techniques
for cooperation with excellent partners at the FAU Erlangen-Nürnberg, the FZ Jülich, RWTH Aachen, and numerous partners in Germany and abroad An excellent international environment to perform sound, high-quality
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morphology with its performance (reactivity, selectivity, efficiency) and degradation under realistic long term deployment conditions Coordination and execution of in-house beam times Collaboration with other
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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using MPI and high-performance computing resources is advantageous, but not necessary Your application should include a CV, motivation letter, copies of university degrees and grades, and contact
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for an ideal balance between stability, performance and price Building a test station for evaluation of electrochemical performance in short stack Physical, spectroscopic, and electrochemical characterization
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scientists on, e.g.: Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and