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, ocean, marine ecosystem, and impact models of different complexity and will include both traditional and new ocean modelling approaches with the final objective of delivering: (i) coordinated and
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reliability of R-Mode, particularly under varying environmental conditions. Key objectives include understanding the physical processes that affect R-Mode signal propagation, quantifying the variability
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. Implement Advanced Characterization Techniques to evaluate material performance and device reliability, integrating electronic control systems including DC-DC voltage boosting and maximum power point tracking
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reduction, uncertainty quantification, machine learning, fluid mechanics. Experience with scientific object-oriented programming languages (C++, Python, or Julia) is highly relevant. Knowledge
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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relation to the use of syntactic structures in coordination with various bodily resources, such as gaze, facial expression, object use, movement in space, etc. Your tasks will involve collecting and
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track record in data modelling, machine learning and deep learning Previous research achievements supported by peer-reviewed publications Excellent knowledge of statistical/machine-learning and deep
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equivalent degree in agriculture, biology or any related field. You are an early career researcher not more than one year after your PhD and you have a proven track record and extensive experience with genetic
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). The empirical research should capture and analyze teaching and learning processes, for example by video analysis or eye-tracking. Development activities for instance may include AI tools, the creation
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), restriction of processing (Art. 18 GDPR) and objection to processing (Art. 21 GDPR). If you have any questions about data protection, you can contact the GWZO data protection officer. You also have the right