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Job description The project: Earth’s dominant cloud type per area covered is Stratocumulus. These low-level, shallow, horizontally extensive clouds cover one-fifth of the Earth’s surface. Changes in
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convective hazards. The model development will be aided by synthesizing observational data and high-resolution numerical simulations to gain new conceptual insights on cloud life cycles. Potential
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of the molecular ISM of galaxies, with a focus on galaxy centres. In particular, this project aims to measure the spatially resolved properties of giant molecular clouds and/or weigh the supermassive black holes
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of machine learning models. · Experience with statistical analysis, multivariate methods, or comparative and phylogenetic analyses. · Experience using high-performance computing (HPC), cloud computing
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-generation cloud chamber technology for exploring aerosol-cloud-precipitation interactions. The postdoc will lead and support cloud chamber experiments, operate a range of instrumentation, and work closely
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. Programming experience in a language such as Python or R. Experience working in a cloud environment such as Microsoft Azure. Experience in writing grant proposals and scientific publications. Strong
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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, Duluth, and Saint Cloud. We sponsor five fellowship programs—sports medicine, hospice and palliative medicine, behavioral medicine, clinical informatics, and human sexuality— and offer continuing medical
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experiments, and connect them into live experimental campaigns so that AI checks every candidate before it reaches the robot deployed on the Genesis American Science Cloud with the Materials Project as
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University of California, Los Angeles | Los Angeles, California | United States | about 23 hours ago
of reproducible bioinformatics pipelines using workflow-management and containerization tools, or high-performance or cloud computing for large genomic datasets. Experience with single-cell RNA-seq analysis using