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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
-informed learning techniques; Development of simulation-based optimisation methodologies, including Bayesian optimisation, derivative-free optimisation, multi-objective optimisation, model calibration, and
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ability to work independently and in a structured manner, good collaboration skills, good ability to express yourself in spoken and written English. Additional qualifications Experience in one or more of
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
approach is based on neural techniques known as SBI (Simulation-Based Inference) [Cranmer et al., 2020]. SBI enables the resolution of inverse problems using generative AI methods and Bayesian statistics
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description The Sars-Covid virus genomic RNA encodes a small number of structural proteins, one of which is the N-protein. This N-protein plays a strong role in packaging the genomic RNA; yet, how this
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, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
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on the theory of deep learning will investigate questions such as the structure and expressivity of emerging neural architectures relevant to space, such as implicit neural fields, continuous normalising
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industrial contexts remains limited due to several structural limitations: · limited interpretability of model behavior; · weak guarantees regarding robustness and reliability
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* structured along five design dimensions — perceptual, narrative, semiotic, interactional, epistemic — whose parameters (visual identity, focalization, dramatic intensity, level of explicitness, metaphor
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(PINNs) and surrogate modelling Time-series modelling and anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and