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Field
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expectations. Preferred Qualifications Previous experience with turbulence over rough walls, porous media, or complex geometries, as evidenced by work history. Knowledge of compressible flow regimes, including
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-on experience with sample preparation and extraction of small molecules from various biological matrices. Knowledge of various extraction protocols (protein precipitation, liquid-liquid extraction, solid phase
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of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as
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19165 Post-Doctoral Fellowship in risk assessment and prioritization and remediation of dumped mu...
pathways of contaminants in diverse environmental and human matrices. Our work involves conducting both environmental risk assessment and public health risk assessment ( https://envs.au.dk/en/about-the
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pathways of contaminants in diverse environmental and human matrices. Our work involves conducting both environmental risk assessment and public health risk assessment ( https://envs.au.dk/en/about-the
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setups, including: management of advanced laser systems, the design and construction of pulse compression systems, and building and operating optical characterization devices. Run existing simulation codes
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and training your own AI-based models for image segmentation or image compression, as demonstrated by Git repositories Experience in supervising students and young scientists Good knowledge of materials
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processing, prepreg systems, resin transfer molding (RTM), vacuum-assisted resin transfer molding (VARTM), and other infusion or compression molding techniques. 2. Advanced Carbon-Carbon Composites for Extreme
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, including filament extrusion, compression molding, injection molding, and fused deposition modeling. This position requires the preparation of ISO/ASTM test specimens for mechanical (tensile and flexural
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between objects. A common way to represent a graph is to use the adjacency matrix associated with the graph. However, adjacency matrices only model networks with one kind of objects or relations between