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Field
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, algorithms, data pipelines, and research software prototypes contribute to optimisation, simulation, sampling, or uncertainty-modelling approaches for complex systems analyse infrastructure, spatial, temporal
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analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Proven experience with reinforcement learning algorithms and implementations. Experience in
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, algorithms, data pipelines, and research software prototypes contribute to optimisation, simulation, sampling, or uncertainty-modelling approaches for complex systems analyse infrastructure, spatial, temporal
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 8 days ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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of these experiments. As part of this, we have developed new algorithms and a completely new web-based platform – EasyNMR - for performing such modelling/simulations. With this position, we seek to strengthen
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to complex ecological networks design and implement novel coordinate-independent numerical algorithms utilising automatic differentiation and pseudo-arclength continuation methods to identify contact points
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learning tools and algorithms. The position will also require you to contribute to the development of data-driven methods. The nature of LDMX as an international project will require you to work
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modeling and simulation • Development in Finite element and alternative discretization methods (e.g. Lattice Boltzmann methods) • High-dimensional algorithms and high-performance computing
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/or R. Develop, maintain, and implement custom scripts for data acquisition, processing, and analysis as well as machine learning algorithms and techniques to classify and predict outcomes based
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for communications metrics Hardware-aware evaluation (desired): participate in SDR/hardware-in-the-loop experiments and help bridge algorithm design with realistic RF/platform effects. Required qualifications Ph.D. in