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will apply semantic modeling on the Multi-User Equipment (EMU) cluster infrastructure aiming at the generation of Explainable Artificial Intelligence (XAI). Mandatory requirements: Ph.D. in
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Dipartimento di ingegneria industriale - Università degli Studi di Padova | Italy | about 1 hour ago
technological development, material characterization, and market analysis to support the implementation of circular production models Where to apply Website https://pica.cineca.it/unipd/ Requirements Additional
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Modeling Core is home to a consortium of postdoctoral fellows who provide modeling expertise for a wide range of projects as integral members of those research teams. Unit URL https://imci.uidaho.edu
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are seeking a Research Fellow to lead the development and
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signaling in endothelial cells and its impact on vascular remodeling. Approaches include mechanistic studies in cultured endothelial cells and mouse models, integrated multi-omics (e.g., bulk and single-cell
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI FISICA E ASTRONOMIA | Italy | 2 days ago
the analysis of ATLAS data in the search for heavy neutrinos within Left-Right Symmetric Models. The goal is to advance the analysis to the publication stage, including the determination of limits and a thorough
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and will contribute to key modeling and simulation deliverables for drug candidates across early and late stages of clinical development, spanning multiple therapeutic modalities. The Fellows will
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The research project studies financial stability through stochastic models
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of the model. Where to apply Website https://www.unibz.it/it/home/position-calls/positions-for-academic-staff/8163-m… Requirements Additional Information Eligibility criteria Eligible destination country/ies
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a