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Optimal Transport for Optimization and Machine Learning Appl Deadline: 2026/02/04 11:59PM (posted 2025/12/19, listed until 2026/02/04) Position Description: Apply Position Description Doctoral student in
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solutions. Through interdisciplinary programmes, living labs and large-scale testbeds, ERI@N drives impactful translational R&D with strong industry deployment. More details: https://www.ntu.edu.sg/erian We
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Materials, Bioinspired Materials and Sustainable Materials. For more details, please view https://www.ntu.edu.sg/mse/research. We are seeking a highly motivated Research Fellow to join our dynamic team
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staff position within a Research Infrastructure? No Offer Description The Optimization Group at Aeronautics Institute of Technology's Computer Science Division (IEC/ITA) in São José dos Campos (São Paulo
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Università degli studi di Roma La Sapienza Dipartimento di Pianificazione Design tecnologia dell'architettura | Italy | 3 days ago
Description he Project is aimed at developing an "Energy Intelligence Data Platform" applied to the management of smart grids that optimize the production from Renewable Energy Sources (RES) and the uses
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applications resource and utilization problems and errors; develop fault diagnosis, isolation and resolution techniques that ensure optimal performance is constantly maintained. Train technical, support and
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freshness metrics (Age of Information) in IoT and edge networks, examining random-access protocols, intelligent backoff strategies, and congestion dynamics. The activity integrates optimization and game
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necessary to make closely related decisions while ensuring that complex constraints are met and optimizing the often-conflicting criteria of different stakeholders. The objective of this research is to design
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strong understanding of social media trends and best practices, and expertise in social media communications to optimize content for maximum engagement by creating compelling narratives. Required Skills
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of the convergence properties and efficiency (assessed through complexity bounds) of optimization methods for the solution of bilevel problems, with applications to quantitative finance and business decision-making