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capability in quantum computing, data-driven modelling, infrastructure systems, computational optimisation, simulation, probabilistic methods, or related areas. This project is part of an ambitious research
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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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concepts from nonlinear system identification, optimisation, computational complexity, and data-driven modelling, with the long-term objective of extending these ideas to modern AI and machine learning
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into the ACT's simulation, analysis and optimisation workflows, fostering the adoption of mathematically grounded machine learning methods in space applications. As an ACT researcher, you will: publish results in
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Application Deadline 27 Sep 2026 - 23:59 (Europe/Warsaw) Country Poland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU
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assessment tool that computes SOT efficiencies through autonomous numerical modelling and quantum transport simulations, and COMPASS, an evolutionary optimiser that proposes new candidate heterostructures
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is to be the “EO innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and
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software and programming environments. Experience with optimisation, simulation, econometrics, or data analysis applied to energy or market data. Knowledge of data centre operations, computing infrastructure
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Jul 2026 - 23:59 (Europe/Madrid) Country Spain Type of Contract Permanent Job Status Part-time Hours Per Week 35 Is the job funded through the EU Research Framework Programme? Not funded by a EU
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Systems At the Institute of Climate and Energy Research – Energy Systems Engineering (ICE-1), our focus is on developing models and algorithms for simulating and optimising decentralised, integrated energy