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methodologies, interfaces, and best practices to ensure model reusability and knowledge transfer across teams. Where to apply Website https://www.imec-int.com/en/work-at-imec/job-opportunities/systemctlm-modeling
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The Multiscale Materials Modelling Group at Maynooth University invites applications for three fully funded PhD positions focused on the design of advanced polymer-hybrid anion-exchange membranes using multiscale
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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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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regional ocean/ice/biogeochemistry models (OBMs) applied to Arctic coastal seas, the properties of terrigenous sediments are rarely integrated into physical and biogeochemical processes. To address this gap
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Biotechnology and Bioprocesses, Membrane Technology, Chemicals and Materials, Resource Recovery, and Modelling & Artificial Intelligence. By harnessing its cross-cutting, interdisciplinary, and transformative
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Biotechnology and Bioprocesses, Membrane Technology, Chemicals and Materials, Resource Recovery, and Modeling & Artificial Intelligence. By harnessing its cross-cutting, interdisciplinary, and transformative
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experiments in gelatine (https://doi.org/10.5194/egusphere-egu25-9211). This model will be tested with analogue experiment results from the MAGMA Lab, run by other members of the project team. The second
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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the DFG-funded research project ICEBAY, which focuses on Bayesian hierarchical modeling and probabilistic inference for temperature reconstruction by combining borehole thermometry and ice-core data. The