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for age estimation, as well as the development of new, robust methods. To this end: Approaches to be implemented and compared: classical point regression, probabilistic regression, and quantile regression
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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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. Desirable Criteria Experience implementing machine learning or deep learning models (e.g., neural networks, probabilistic learning methods). Knowledge of state estimation techniques, such as Kalman filters
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(estimation filter) methods, while preserving the convex character of the optimization problems to be solved. An alternative path to consider will be the use of convex relaxations. As the expected contributions
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-view learning that are robust under distribution shift and missing modalities. The key objectives include: Develop scalable Bayesian deep learning methods for uncertainty estimation in modern neural
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presentation works such as high-quality paper writing. Job Requirements: Preferably Master in computer engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical
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for building Data+AI systems. Wring research papers of high quality based on research results Building deployable Data+AI systems based on the research results. Job Requirements: Preferably PhD in Computer
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focused on immune repertoire profiling. Building on the foundational TIRTL seq (Pogorelyy and Kirk et al., Nature Methods 2025) method, we provide cohort scale T cell and B cell receptor (TCR and BCR
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estimation method for the hazard function within the framework of cure models. This method aims to provide flexible, data-adaptive estimators with optimal statistical properties (optimal convergence rates and
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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