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for seasonal prediction using hybrid physics-machine learning models in R&D item Research on Seasonal Meteorological and Oceanographic Forecast Simulator under Development of Integrated Simulation Platform
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 8 hours ago
science and technology to reduce risks and costs within the EM regulatory framework. The Scholar will participate in learning and development opportunities, be actively mentored by EM staff, as
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, Geospatial Science, Data Science, Computer Engineering, or a closely related field, with an emphasis on machine learning, AI, remote sensing, computer vision, or interdisciplinary data science applications
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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machine learning. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time. Starting date by
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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application and machine learning. • Author of at least 2 research papers in topics related to the project. • Knowledge on advanced control systems and optimization (please attach a description of your