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specimens. The postdoc will contribute to the development of hybrid modeling and identification approaches that combine classical constitutive frameworks, numerical simulation, and machine learning. The work
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geomatics, spatial modelling and simulation, and model exploration and evaluation (OpenMole platform https://openmole.org/ ). The recruited individual will also work directly with the project's entomologists
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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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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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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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of Coastal Systems (COS) of the Royal Netherlands Institute of Sea Research (NIOZ) is looking for a highly motivated and proactive postdoctoral researcher to carry out simulations with a hydro-biogeochemical
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research team members to ensure all project deliverables are met. Identify, simulate, and analyse ecosystem service models using GIS, remote sensing, and field data. Manage, process, and analyse ecological
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Position in Theoretical Quantum Many-body Physics and Quantum Sensing at Indiana University Bloomington Fields: Quantum many-body theory, AMO–condensed matter interfaces, advanced numerical simulations