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Field
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baseline data for estimating the effectiveness of proposed restoration measures for species, habitats, and ecosystems. The Positions The selected candidates will work in close collaboration with Dr. Martin
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. Experience with uncertainty quantification, Bayesian inference, inverse modelling, parameter estimation, or model calibration. Experience with high-performance computing, surrogate modelling, reduced-order
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to real-world program, product, or policy evaluation contexts. Professional experience building regression, generalized linear models, hierarchical/multilevel models, and Bayesian approaches The ability
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to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis
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or application. Strong technical expertise in one or more of the following areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and
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on concepts like Nash equilibrium SPNE, bargaining, Bayesian Nash equilibrium, auctions, and signaling games. You will also grade very short quizzes (probably around 5 - 8 of these). You will have to enter
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that explicitly account for drift and open-set contamination. O2: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal
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the theory of brain-inspired algorithms and apply them to complex, real-world problems. The successful candidate will join an interdisciplinary team of computer scientists, mathematicians, engineers, and
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thereafter. The position will be available for a two-year period, with possibility of extension. You will be part of a research environment focusing on estimating greenhouse gas (GHG) emissions, reactive
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the same systematics identified in the observables. d) Estimation of cosmological and “nuisance” parameters using Bayesian methods. 4. The research activities provided for the post-doc assignment will