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, from basic research to more applied projects often focusing on sustainable chemistry but also advanced analytical methods of chemicals and materials. The department has a very well-developed research
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Innovation: Exploiting and developing NDT technologies tailored specifically for Dutch half-joint geometries. Data Analytics: Developing advanced analysis methods to identify and characterize local damage
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to develop a practical, risk-based framework to support evidence-led decision-making on wastewater reuse in Irish agriculture, particularly in the context of increasing water scarcity and climate extremes
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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electrical characterization. A strong interest in experimental work and willingness to develop independent lab skills are essential. Good written and oral English communication skills. Prior knowledge of micro
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the Marie Skłodowska-Curie Actions (MSCA) grant on WindConnect project. The project The WindConnect (wind farm control and integration in sector-coupled power systems https://cordis.europa.eu/project/id
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the ERC Synergy DISRUPT project, which aims to fundamentally rethink PLL architectures and develop new circuit techniques that significantly improve spectral purity, robustness, and efficiency. The research
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of the observation receiver used to measure the transmitter output and extract distortion information. This position is part of the ERC Synergy DISRUPT project, which aims to develop new architectures for observing
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(SustainCARE, https://doi.org/10.3030/101220359 ), led by Dr. Yun Liu. SustainCARE brings together heritage science, building physics, advanced modelling, and climate-conscious preservation, offering a unique
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
, reference no. 2023.18249.ICDT, financed by national funds through FCT/MCTES (PIDDAC Workplan: Development of methodologies and machine learning algorithms for the detection of anomalous behaviors in flow rate