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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
-driven and physics-informed learning techniques Development of simulation-based optimization methodologies, including Bayesian optimization, derivative-free optimization, multi-objective optimization
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ecology. A strong quantitative mindset is essential, including good skills in data analysis using R, Python or similar tools. Experience with trophic ecology and Bayesian approaches would be an advantage
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to accelerate formulation discovery. Experimental data will be organised into a comprehensive database and analysed using statistical learning and Bayesian optimisation, establishing a closed-loop framework
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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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Experiments (DoE) and Bayesian optimization, manage research data using the NOMAD research data infrastructure, and apply data-driven optimization strategies. Analyze and interpret experimental data
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, Bayesian inference, model calibration, and Markov Chain Monte Carlo methods, uncertainty quantification, statistical modelling, and Gaussian processes, machine learning for time series, sequence-to-sequence
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research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven epidemic forecasting, transmission modelling in the ASEAN region
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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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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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to material properties, environmental loading, sensor data, and model fidelity. Bayesian and stochastic techniques will be used to propagate uncertainty through diagnosis and prognosis models, enabling