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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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energy recovery linac (ERL) demonstrator at IJCLab, Orsay. ERLs offer a promising way toward the development of future colliders, particularly by providing excellent beam quality while drastically reducing
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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 12 days ago
expressivity and complexity of neural networks and neural operators, as well as on the development of novel algorithms connecting theory with practice. For more information contact Dr. Ahmed Abdeljawad
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The Computational Protein Engineering (CPE) group at The Novo Nordisk Foundation Biotechnology Research Institute for the Green Transiation (BRIGHT) is developing novel methods to engineer proteins
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About the Opportunity Job Summary: The Open6G group at the Institute for the Intelligent Networked Systems (INSI), Northeastern University, is leading research and development, testing and
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of pinching plasmas. This research associate will work with the Michigan State University (MSU) team to develop new scalable algorithms inside of the Parthenon framework, an AMR performance portable framework
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region of Portugal, although other tasks may be considered according to the profile of the recruited researcher. The researcher will be responsible for implementing spatial analysis algorithms, developing
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by combining psychological profiling, biological lab data, physiological time series, and sensor data. The postdoc will play a leading role in developing and implementing predictive algorithms designed
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-transparent materials, and the utilisation of deep learning algorithms to accelerate computational solutions. Scientific Objectives Develop a self-contained finite volume solver for solidification of multiphase
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Transparency). The main tasks will be to: Develop AI-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness