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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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, and machine-learning-based analytics. The research work at NTNU will focus particularly on automation, robotics, mechatronic design, sensor integration, and intelligent experimental systems required
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
8 Jul 2026 Job Information Organisation/Company Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID Department DRH Research Field Engineering » Electrical
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vacation pay of 8%. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process . An
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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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requirements for admission to the faculty's Doctoral Programm e, see Section 6-1 of the PhD regulations for more information. You must have a relevant Master’s degree in computer science or equivalent. Your
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stays at foreign educational institutions Support to education activities in courses within the Software Engineering area Other career-promoting work, such as learning grant preparation fundamentals Be
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 1 month ago
teams and a variety of perspectives enrich our work and our daily collaboration. In a continuous process of learning and reflection, we aim to ensure that all our employees can be themselves and feel a
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Principal Investigator: Maciej Behnke, PhD, DSc Scholarship amount: PLN 3,000 per month Planned duration: 36 months, starting 1 September 2026 Where to apply E-mail [email protected] Requirements Research
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results. ● Deploy the results developed in the first stage to linear value function estimation problems in reinforcement learning theory. Establish the foundations to generalize the results to non-linear