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adsorption/incorporation of PO4 into solid phases of marine snow. You will test the performance of your model by simulating experimental and field data in collaboration with the PHOSFLUX PhD candidate. Where
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analysis by integrating diverse datasets (e.g., in situ observations, remote sensing products, model simulations) to inform model development, calibration, and validation. Collaborate with a
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collaborators. The activities will include: • Electromagnetic modeling and numerical simulations (e.g., FDTD, FEM) • Design of metasurface architectures based on dielectric materials available at CRHEA
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response to climate forcings for different past climates. · Perform and analyze global model simulations. · Collaborate with IPSL Earth System model developers to ensure consistent integration
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machine learning and computer simulations. The focus of the PhD project will lie on developing machine learning models for clustering, classification, regression and reinforcement tasks to work with
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of Pisa) and Dr. Stan Van Gisbergen (SCM, Holland) https://www.scm.com/ , who will also serve as industrial mentor. DC9 - Objectives: Apply Machine Learning force fields and sampling methods to model bio
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Keen to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering! About us Our
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on advanced internal combustion engines, with an emphasis on turbulent-jet ignition systems and structural-mechanics modeling of cylinder-kit components. The ideal candidate will possess a strong background in
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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the project team, you will ensure the simulation of drone missions using state-of-the-art tools for AI learning and demonstration. You will be responsible for producing training data for vision models and