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• Develop analytical protocols for dedicated target compounds • Systematically vary reaction conditions to probe for isotope effects of different reaction steps • Synthesize labelled substances in a
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 13 hours ago
The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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central scientific challenge will be to learn integrated representations of forest ecosystems from datasets with very different characteristics, resolutions, coverage, and levels of supervision
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measures that are robust, reproducible and relevant to assessments used in patients and mammalian disease models. Your research will combine different approaches to: Develop and standardise behavioural
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methods tailored to ILC. The PhD researcher will fine-tune and benchmark pathology foundation models using multi-site H&E and immunohistochemistry whole-slide images. The aim is to learn representations
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stress and an online learning platform with 100+ different courses; 7 weeks birth leave (partner leave) with 100% salary; partly paid parental leave; the possibility to set up a workplace at home; a
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thinker, eager to learn new experimental techniques, and enjoy collaborating with researchers from different disciplines as well as industrial partners. Furthermore, you meet the following requirements: You
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high-resolution mass spectrometry, in vitro pharmacological characterisation of new psychoactive substances, as well as metabolomics and machine learning. As a PhD student, you devote most of your time
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to the development of sustainable materials for the hydrogen economy. You are an independent thinker, eager to learn new experimental techniques, and enjoy collaborating with researchers from different disciplines as