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Field
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 4 days ago
computationally intensive, relying on iterative time-marching algorithms to reconstruct heating environments. This limits their use in more advanced computational analyses, such as uncertainty quantification
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accelerator laboratories, the R&D work of the Liverpool Hadronic Matter Group focuses on the development of Monolithic Active Pixel Sensors (MAPS), the state-of-the-art silicon sensor technology for high
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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processing, inverse problems and data science with emphasis on analysis, optimization, numerics and algorithmic solution Collaboration in interdisciplinary cooperation projects and third-party funded projects
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fabrication of bioelectrical platforms with clinical translation as the ultimate goal. Using unique bioelectrical (physical and chemical) sensors and actuators will allow us to tackle grand biomedical
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Models with Algorithmic Reasoning Tasks We are seeking a postdoctoral researcher to contribute to our lab’s mission of aligning machine learning (ML) models with algorithmic reasoning tasks. Our goal is to
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training
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manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for
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Iterative Algorithms: Optimization and Control.” About the Project The focus of the project is the analysis of iterative algorithms arising from time discretizations of nonlinear evolutions of various kinds
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from