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. Our lab studies how co-translational processes regulate gene expression and is newly focused on leveraging these insights to create novel molecular tools. One long-term goal of our research program is
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environment is a fundamental human right. We believe that ensuring a prosperous global future depends on the ability to support local people and communities everywhere. By working in and across multiple
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productivity while reducing external inputs. In parallel, the lab is expanding efforts to understand microbiome-associated phenotypes that contribute to drought tolerance and soil water retention. This includes
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integration and supporting the energy transition towards a more efficient, circular, and reliable energy system. Our work covers coordinated planning and operation across multiple energy systems, renewable
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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organization, and how these properties are altered by pathological processes across multiple biological scales. The position is available in the team of Dr. Andra C. Dumitru in the nBIO group at LIBST, UCLouvain
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interdisciplinary team. Please keep your eyes open for related positions advertised in parallel to this one. If you see yourself to be qualified for more than one of them, please re-submit your application for each
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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to improve the modelling and governance of biodiversity under uncertainty. The project develops process-explicit, hierarchical models that capture key ecological dynamics, integrate diverse and incomplete data