305 algorithm-development-"https:"-"Fraunhofer-Gesellschaft"-"UCL" Postdoctoral positions
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Field
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allows the successful applicant to choose a flexible balance between advanced machine learning method development and exciting applications on molecular data, including biomedicine and drug discovery
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 16 hours ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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classical and quantum modeling, quantum algorithms and computation, and battery materials and technology Further development of your personal and interdisciplinary strengths through a range of training
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to support a two-year Small Business Technology Transfer project (STTR) funded by the National Science Foundation. The successful candidate is expected to support the development, simulation and
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of autonomous flow reactors for chemical synthesis. The project aims at 1/ developing a new optimization Bayesian algorithm and 2/ improving the process-control software already developed in the team
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systems, understand the design and development decisions that propagate social biases, and develop theoretical and algorithmic approaches to mitigate them. Key responsibilities include developing bias
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explainable. Our team builds upon over 20 years of expertise in image processing algorithm research and more than 15 years of experience in developing open-source software for image processing and digital art
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-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments
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collaboration with the project team, this work aims to strengthen the interaction between modelling, algorithm development, and system-level implementation, thereby enhancing the overall scientific and
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
models. The project's central goal is to move beyond algorithm-independent bounds and develop rigorous theory that unpacks the generalisation properties inherent to the training and sampling processes. A