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Are you interested in this position? Please send your application via the 'Apply now' button below beforeNovember 15, 2025 and include: A full CV including your publications, awards, and working experience. Your CV should demonstrate the scope of your work and education and indicate a good fit...
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Vacancies PhD position on Sustainable Mobile Networks Key takeaways Mobile networks, as a key component of digital connectivity and computing infrastructures, play a crucial role in modern life
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wider socio-demographic contexts and spread through social networks. Using survey and register data, you will develop typical household profiles, design an agent-based model, and test interventions in
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a PhD stipend in the field of Quantum Information Processing in Networks. The stipend is
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PhD Position in Accelaration of Energy Transition through Networks Faculty: Faculty of Social and Behavioural Sciences Department: Social Sciences Hours per week: 36 to 40 Application deadline
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position is for 36 months and is funded by the ENERPOL MSCA Doctoral Network. ENERPOL is a European research and training network (MSCA Doctoral Network ) exploring the role of households in the green energy
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the official date 01/11/2025, but unofficial preliminary contacts are encouraged. Salary will be 3200 euros per month net. The title of the PhD project in Trieste (DC7) is: "New Methods for Predicting Chiro
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thalamocortical networks in humans and elucidate their critical roles in cognitive functioning, as well as in developmental and neurodegenerative disorders, thereby advancing our understanding of brain health and
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real