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14 Jul 2026 Job Information Organisation/Company KU LEUVEN Research Field Computer science » Modelling tools Computer science » Programming Computer science » Systems design Technology » Computer
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | about 2 months ago
, socially situated interactions, with two central requirements: (i) robustness to real-world perturbations, with quantitative estimation of the reliability of extracted cues, and (ii) continuous adaptation
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Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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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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of neural networks, information-theoretic and optimal-transport perspectives on representation and generalisation, probabilistic numerics and Bayesian deep learning, and emerging frameworks for scientific
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, optimization, and characterization integrating imaging, experimental metadata, and diffraction outcomes. Design and deploy computer vision methods to detect and track crystal growth. Develop closed-loop
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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 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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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