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decision neuroscience. • This requires programming of experiments with human participants, typically in computer-based experiments in a behavioral or neuroimaging laboratory, but also in online
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, computer/data science or relevant topics in other fields. Doctoral dissertation must be submitted for evaluation by the closing date. Only applicants with an approved doctoral thesis and public defence are
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comparing models with entirely different structures and parameter counts, whether comparing linear regression against mixture models or decision trees. MML is strictly Bayesian, requiring prior distributions
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taken using different approaches such as Bayesian learning, evidential learning or conformal prediction to provide AI systems with mechanisms allowing them to “know when they do not know", provide
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. This large multimodal dataset allows us to estimate and test different computational models of the decision and learning processes. One postdoc is currently working on the MEG and iEEG data, and one PhD
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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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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
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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including