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Field
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leading the establishment of a centralized repository for genomic data and metadata, with a strong emphasis on existing and novel methods for the evaluation of genome quality, indexing and clustering
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 26 days ago
modelling, foundation-models, or explainable AI. Familiarity with training on GPU clusters, experiment tracking, containerization, and reproducible pipelines. Evidence of research potential for top venues
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change The opportunity to interact with experts from Imperial, the UK, and Europe in both aerospace engineering and atmospheric science Access to a state-of-the-art, high performance computing cluster You
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finalization of the foundations of dual-tracer imaging using a GATE-based Monte Carlo simulation Implementation of the developed algorithms within our modular, C++-based and cluster optimized PET image
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, and of the Austrian Cluster of Excellence (quantA), advancing basic research in quantum sciences, aiming to expand the frontiers of knowledge and thus being the engine for future innovations. You will
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to provide hands-on support in the lab to research being carried out by MSc students, PhD- and postdoctoral researchers in the QG&QI, QMat and SM research clusters. You will play an important role in the QG&QI
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, established to foster world-class mathematics discipline clusters. It integrates talent cultivation, scientific research, and social service into a synergistic and innovative whole. To gather outstanding global
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, PhD- and postdoctoral researchers in the QG&QI, QMat and SM research clusters. You will play an important role in the QG&QI research cluster, for example building new laser systems, helping new students
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analysis: Applying geospatial methods (GIS mapping, geographically weighted regression, spatial clustering) and temporal approaches (time-series analysis, distributed lag models, case-crossover designs
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for NCDs. This will involve: Spatial analysis: Mapping and modelling environmental exposures at fine spatial resolution using GIS tools, geographically weighted regression, and spatial clustering techniques