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Mechanisms for Vehicular Networks Summary of the Scholarship Objectives: The scholarship aims to design, implement, and evaluate optimization solutions for vehicular networks based on Reinforcement Learning
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production. By developing hybrid architectures combining ontologies, generative models, reinforcement learning, and uncertainty quantification, the PhD project addresses the challenges identified by ICCARE in
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to design and produce novel energy harvesting devices for the development of self-driven neural interfaces. Where to apply Website https://jobs.icn2.cat/job-openings/850/predoctoral-researcher-advanced
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INSTITUT CATALÀ DE NANOCIENCIA I NANOTECNOLOGIA | Universitat Autonoma De Barcelona, Cataluna | Spain | 3 months ago
APPLICATION AREAS OF RESEARCH Health Energy Environment Information, communication and quantum technologies Candidates can review the research lines here: http://icn2.cat/en/research During the application
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APPLICATION AREAS OF RESEARCH Health Energy Environment Information, communication and quantum technologies Candidates can review the research lines here: http://icn2.cat/en/research During the application
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SD-26109- POSTDOCTORAL RESEARCHER IN AI-BASED ENERGY MANAGEMENT OF RESILIENT MICROGRIDS WITH SECO...
our website: https://www.list.lu/ How will you contribute? You will contribute to the ENERGY-RESILIENT-UA project, which aims to support vulnerable communities in Ukraine through resilient, low-carbon
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(e.g. Agentic Reinforcement Learning), evaluation, tool use, agentic harness, or retrieval-augmented systems. Internship/full-time experience from research, engineering, or algorithm-development roles in
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potential. We are strong supporters of open science (publishing, source code, data). You will also be expected to assist in teaching activities (student supervision, labs) related to your subject area. About
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supporters of open science (publishing, source code, data). You will also be expected to assist in teaching activities (student supervision, labs) related to your subject area. About the Future Network
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components. You will explore how learning-based methods, such as imitation learning and reinforcement learning, can be integrated with model-based low-level controllers and multimodal sensing to enable contact