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out research activities, including reports, studies and scientific articles; Creation of algorithms, code snippets and development of statistical methods to achieve the desired results; Collaboration
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the development of efficient algorithms and codes for multilinear algebra, with a particular focus on the use of innovative parallel programming models and tools. In the context of this task and as part of the Exa
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plan (e.g., microfluidic channel optimization, polarization-dependent scattering studies, spectral imaging implementation, or algorithm development). Planning experimental campaigns, simulations, and
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of control software and data management systems for the automated laboratory. Development and deployment of AI algorithms for adaptive experimental planning, optimization of experimental space, and automated
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to be developed: Analyze iEEG data. Develop multimodal algorithms. Perform the characterization of the epileptogenic network. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 9 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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University Abu Dhabi, seeks to recruit a motivated research or postdoctoral associate to lead the design, development, and validation of material, control systems, and algorithms for next-generation soft
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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. Specific responsibilities include, but are not limited to, the following: Develop the core tensor network algorithm for full RIXS cross-section simulations. Benchmark simulation results against ED codes
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such as scalable identification algorithms, uncertainty quantification, and the integration of learning-based models with formal verification. We offer a supportive, inclusive, and collaborative research