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Field
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Aerial Vehicles (UAVs) sent to perform a mission, e.g. search and rescue operations, intelligent transportation systems and wireless sensor networks. The data can vary from image and video, GPS, GSM
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in English Candidates without a master’s degree have until 01.09.2026 to complete the final exam. Desired qualifications: Solid foundation in Bayesian statistics, empirical Bayes methods and advanced
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communication and networking skills. The ideal candidate is a dynamic and independent researcher who works effectively in interdisciplinary teams and is adaptable to diverse ways of working and doing science
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nets, stochastic processes, Bayesian networks, etc.), who could integrate well into the laboratory. In coordination with the platforms of the laboratory, the recruited person will be responsible
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the requisite experience. A2 Knowledge of mathematical and statistical methodologies including several of: Statistical modelling and inference, Bayesian statistics and probabilistic modelling, Inverse problems
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-informed / simulation-aware modeling Efficient algorithms for design-space exploration (e.g., surrogate modeling, Bayesian optimization, differentiable programming) Hybrid approaches combining data-driven
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The successful candidate will join a global network of Simons Postdoctoral Fellows in Strong Gravity and Black Holes that are part of the Simons-BHSG. This new, multidisciplinary and multinational collaboration
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designing/programming experiments, recruiting/running participants, developing and using computational modeling approaches (Bayesian, RL, neural networks) to analyze behavioral and neuroimaging data
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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intelligence, machine learning, big data and network analysis, computational and Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented