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Field
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Description You will: * Lead MEG head-cast data collection for a visuomotor reaching/interception study, ensuring robust synchronization with video-based kinematics and eye-tracking, and enforce rigorous
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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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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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, 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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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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) as well as the HEP group in DAMTP. Applicants should have or should soon receive a PhD in a relevant area of physics or astronomy, and have an established track record of original research and
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mechanisms. Proven track record of productivity (first-author publications); strong scientific writing skills to apply for funding/fellowships. Works in a laboratory environment with potential exposure
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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