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scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective
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algorithms developed for the mission. The aim of this project is to develop and test enhanced L2 algorithms for the four hydrological parameters of HydroGNSS, leveraging a combination of machine learning
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embedded in the context of the RVO project “SLDbatt”, in collaboration with Dutch research organisations and ESS industry partners. The SLDbatt project aims to develop and deploy battery technologies towards
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students to become experts in a specific domain of choice. This vacancy is explicitly targeted at candidates interested in algorithmic biases and developing methodological approaches to tackle this challenge
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system-level grid-connected LDES models for grid support Research, design and development of control algorithms for optimal operation of grid-integrated LDES; Develop a co-simulation framework to analyse
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. The position is embedded in the context of the RVO project “SLDbatt”, in collaboration with Dutch research organisations and ESS industry partners. The SLDbatt project aims to develop and deploy battery
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research is developing structured, LLM-readable document representations that enhance accuracy, facilitate automation, and improve interoperability across different model types used within the organization
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website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Algorithmic Optimisation of Stowage for a Cargo Return Vehicle You will help develop a numerical optimisation
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Are you interested in challenging deep learning at its core? And specifically, do you want to perform cutting-edge research and develop novel advances in hyperbolic deep learning for computer vision
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analysis has not yet been conducted. This leaves stakeholders unable to statistically and systematically assess the quality of official network maps. Could this be done differently? Is it possible