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Field
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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resonance imaging data among other imaging modalities, machine learning methods for prediction, treatment effect estimation, and contribute to understanding brain biomarkers of Alzheimer’s disease and their
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 days ago
repeated over several trials (to collect multiple samples) before making a decision. For these applications, statistical methods for hypothesis testing and estimation are needed, with formal guarantees
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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will primarily work on interdisciplinary projects to examine how computing students develop and enact ethical agency within the context of human-computer interaction (HCI) and user experience (UX) design
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guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
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-Computer Interaction, Information Science, Computer Science, Cognitive Science, Design, Sociology, Psychology, Education, or a related field Demonstrated expertise in mixed-methods research, including
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. We will develop a posteriori estimation tools to obtain error indicators that will guide mesh adaptation. These indicators must be specifically designed to account for the anisotropy of the field. Our
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of advanced modelling and machine learning methods, and may involve the following areas: Dimensionality reduction. Data-driven methods for estimating dynamical models Data-driven methods for estimating
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evolutionary). The project aims to approximate a Boltzmann distribution associated with an objective function that is difficult to optimize in order to solve complex optimization problems. This probabilistic