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Austrian Academy of Sciences, the Johann Radon Institute for Computational and Applied Mathematics (RICAM) | Austria | 10 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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will focus on developing theoretical and algorithmic foundations for goal-oriented, semantics-aware communication enabling timely and reliable cloud-to-agent interactions. For more details on semantic
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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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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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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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3GPP compliant 5G/6G NR NTN OFDM waveforms Develop and analyse signal processing and/or machine learning algorithms for joint channel, delay, Doppler and carrier phase estimation, remote object ranging
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and limited resources (fleet size, mobile and fixed charging infrastructure). This project aims to address these challenges by developing novel mathematical models and algorithms to support real-time
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mathematical theory and algorithm development as well as engineering methods that enable robust and efficient practical solutions. As society and technology evolve toward increasingly large‑scale, data‑intensive
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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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Discovery”, with a strong scientific and environmental ambition: developing lower-footprint AI methods for real inverse problems in nondestructive evaluation. The topic has already passed the first ENACT