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Job Description Are you passionate about leveraging IoT, machine learning, and optimization to make energy districts and communities more sustainable? We are looking for a highly motivated and
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machine learning, computational modelling, and advanced data analytics can accelerate not only the discovery, characterization, and optimization of materials but also project assessment and communication
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platforms. Experience in high-performance computing or working with large-scale simulation environments. Prior work involving model calibration, optimization under uncertainty, or scenario analysis
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researchers passionate about making a difference in the field of science. About SCC SCC is a climate cluster organization established to create an optimal framework at SDU for groundbreaking, excellent and
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, generative design, building performance optimization, digital design methods (e.g., predictive modeling, multi-agent systems and algorithmic techniques for architectural design), digital design epistemologies
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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characteristics, assessments committees’ configuration and process) and 2) the resulting outputs for applicants depending on grant decisions. The research will leverage artificial intelligence (AI) to optimize
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial
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CMOS analog/mixed-signal circuits (Cadence, Spectre, etc.) Optimize circuits for power, noise, and area constraints Prototype and validate circuits with EEG signals and wearable sensors Collaborate with
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Electronics Engineering. Currently, the section focuses on three research fields: 1) Drones for Infrastructure Inspections and Interactions 2) realtime processing, and 3) optimized power electronics