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Field
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measurements are most informative and guiding where, when and how to observe next. By combining Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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, remote object ranging and tracking in LEO constellations, and monostatic and bistatic SAR imaging using communication waveforms Design and evaluate self-interference cancellation (analog/digital) and
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systems, and analytical methods to meet partner objectives Prepare, analyze, and interpret complex data; disseminate findings through peer-reviewed publications, presentations, and technical reports Write
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activities of the ARES’ group in general, and, more specific, to the objectives of the Pathfinder PhotoniX project funded by EU EIC Accelerator. The project focuses on the design and development of innovative
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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, macroinvertebrate collection, and stable isotope analysis. The successful candidate is expected to have extensive experience in aquatic ecology, coding in R, and an ability or willingness to learn Bayesian modeling
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many