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nanoparticles and reactions at the atomic-level by combining path-breaking advances in electron microscopy, microfabricated nanoreactors, nanoparticle synthesis and computational modelling. The radical new
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scientists covering a broad range of expertise in photonics and electronics. The Project in Short The project focuses on developing numerical modeling and optimization tools to explore the information
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Cadence Virtuoso, Spectre, or Mentor Graphics. Understanding of semiconductor device physics, especially MOSFET operation, modeling, and scaling. Strong analytical and problem-solving skills, with
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optimization. We are looking for a candidate who is motivated by both technical curiosity and making a real-world impact. Ideally, you: Have experience with AI models (e.g., graph neural networks, supervised
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wearable and ambient IoT sensing systems for activity and health monitoring. Implementing embedded AI models for anomaly detection and behaviour analysis. Working on digital twin and serverless IoT
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intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence
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of the following areas and an interest to develop within others: Protein chemistry Enzyme kinetics and kinetic modelling Experimental physical chemistry Electrochemistry Assay development and
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designs, building effective and conceptual models to inform our theoretical understanding, and developing code and theory frameworks to address new topological phenomena. Depending on the project’s results
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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results with AI models and system simulations to create a digital twin of the PtX process for predictive optimization and scenario analysis. Funding This PhD position is generously funded through the Villum