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ferroelectric memory, photonic integrated circuits and next-generation telecommunications. You'll work at the exciting intersection of experimental materials science and materials informatics, collaborating in
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demonstrated research experience with foundation models , including the evaluation and adaptation of pre-trained models, fine-tuning strategies, and the development of new model architectures or learning
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methodological background in machine learning. The ideal candidate has demonstrated research experience with foundation models, including the evaluation and adaptation of pre-trained models, fine-tuning strategies
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characterization of NIR-emitting OLED devices Close collaboration and active scientific exchange with project partners Analysis, interpretation, and documentation of experimental results Preparation and publication
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of cooperative effects in charge transport, proton dynamics and electrochemical conversion in solids and at electrochemical interfaces. Your tasks The research focuses on experimental investigations and theory
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-flow UV-Vis, to quantify mediator-booster reactivity and component dissolution during operation, developing a firm understanding of how material formulation and experimental parameters govern charge
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, to quantify mediator-booster reactivity and component dissolution during operation, developing a firm understanding of how material formulation and experimental parameters govern charge transfer at the liquid
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degree in Physics/Photonics/Engineering or equivalent Experience: Laser applications (measuring, processing, etc.), optical system design, assembly and usage Skills: Hands-on experimental mindset, strong
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Have very good lab skills Have preferably experience in the biomedical domain and in image processing Have preferably experience using ML models and tools (e.g. radiomics) in image processing and very
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results-oriented team player. Ideal profile includes following competencies and experience: Strong programming skills, deep statistical knowledge and a proven track record with machine learning Solid