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Field
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Twins & Energy Analytics Forecasting & Optimization Algorithms Green Hydrogen Technologies ICT Applications in Energy Systems GIS Applications in Energy Planning Interdisciplinary and innovative research
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on developing the simulation models, data models and algorithms required to enable connected cross-disciplinary design and optimisation, laying the foundations for more integrated and intelligent engineering
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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Hands-on experience with machine learning algorithms and developing/testing complex software systems Basic understanding of hardware/embedded software development Additional Requirements (MSCA Eligibility
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, in collaboration with the team responsible for the development of the ApneaScreener algorithm. Knowledge of data analysis tools, particularly Python and R, will be valued to support the interpretation
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The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic architecture of important crop traits, such as grain yield, adaptation
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-of-information metrics. Propose computational algorithms to estimate these metrics. Design and execute simulation studies to evaluate the above. Develop and test statistical software. Write user-friendly guidance
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training datasets; Design and carry out laboratory experiments to produce representative experimental training data; Develop physics-informed machine learning algorithms, trained on both numerical
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algorithm of the PotionDB system (https://github.com/AndreRijo/potionDB ); • Evaluate the proposed improvements in a geo-distributed context, using appropriate benchmarks; • Document the proposed improvements