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collaboration, LIST and an industrial partner are willing to develop innovative electroplated copper-based composites reinforced with metallic/ceramic and Carbon nanostructures. The aim is to significantly
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experience in modeling of atmospheric composition. Additional Qualifications Experience with the GEOS-Chem model and with inverse analyses, strong scientific programming skills. Experience with inverse
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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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experience in modeling of atmospheric composition. Additional Qualifications Experience with the GEOS-Chem model and with inverse analyses, strong scientific programming skills. Experience with inverse
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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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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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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Materials Science, Characterisation Materials Science, Defence Composite Materials, Functional Composite Materials, Energy, Nanomaterials, Low Dimensional Materials, Biomaterials Materials, Biological
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 7 days ago
preferential factor if the candidate demonstrates proven experience in discrete element modelling, particularly using YADE. Additional Information Benefits Monthly maintenance allowance: the amount
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experience in data-knowledge fusion-driven dynamic modeling of complex structures, specializing in uncertainty quantification of nonlinear systems and intelligent model order reduction methods, with at least