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al. 2019] and point-force Lagrangian models, with advanced post-processings [Vegad2024]. This work will be carried out with the YALES2 high-performance platform. Where to apply Website https
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 27 days ago
the discrepancy between theoretical predictions and the actual observed behavior. The objective is to develop model-based artificial neural network tools that combine the strengths of traditional numerical
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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, combined with a predictive operational insights model to gain superior operational performance. Employed and supported by an academic team from the University, you will be based at ELE Advanced Technologies
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, and generate high-quality datasets for predictive microbial modelling and risk assessment. Responsibilities include contributing to the design and execution of food challenge studies, integrating
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different hypotheses, this thesis will combine an empirical in-situ approach (field surveys and plot monitoring through on-farm experimentation) with a predictive in-silico approach (modeling and meta
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modes, effects, and criticality requires deep domain knowledge and careful analysis. Collecting High-Quality Sensor Data. Simulating Realistic Fault Conditions. Developing Reliable Fault Prediction Models
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an
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opportunity to work in a top-tier interdisciplinary setting. This is what you will do You will develop predictive computational models to capture the formation and heterogeneous structure of microthrombi, with
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colleagues on multi‑omics data integration and analysis. You will also work with AI experts to help implement predictive models that improve guide design and functional genomics workflows. You will join an