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interest in information processing in humans and computers, and a particular focus on the signals they exchange, and the opportunities these signals offer for modelling and engineering of cognitive systems
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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experimental research-based technology integration, modeling, control, and data-driven analysis and toolset development. Here is the chance to work in a pleasant team atmosphere, dive into research and at
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. Your overall focus will be to help develop a semantically integrated data ecosystem that links experimental data across our pilot plant and coatings science center using ontologies and knowledge graphs
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qualifications: Experience with GHG flux measurements (eddy covariance, chambers) or nutrient flux monitoring. Skills in process-based modelling or ecosystem resilience assessment. Teaching and supervision
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qualifications: Experience with GHG flux measurements (eddy covariance, chambers) or nutrient flux monitoring. Skills in process-based modelling or ecosystem resilience assessment. Teaching and supervision
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-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis
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of software engineering techniques to ensure interoperability of their modelling and mining ecosystem. This position will run from October 2025 (or shortly after) and it has an expected duration of 24 months. A
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Post Doctoral Researcher in Human-Centered AI for Software Engineering, Department of Electrical ...
of responsible and human-centered AI in software engineering (AI4SE). The AI4SE1DK project addresses urgent industry needs by investigating how Large Language Models can be effectively, ethically, and sustainably