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Field
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for Digitalization. This project aims to advance privacy-preserving techniques for data analysis and task automation, ensuring robust protection of sensitive information. The focus will be on developing
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applications, such as wireless and wireline communications, satellite systems and high-accuracy radars and sensors for industrial automation. Multiple research associate / PhD candidate positions are available
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computer vision and machine learning methods and adapt new algorithms to automate inspection procedures of PV plants. Given the data captured by a remotely operated drone, we first investigate the required
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
) to engineers and automated systems •Validate the system’s resilience, scalability, and practical relevance using real-world and representative datasets, with evaluation of technical performance and potential
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of innovative characterization techniques for electrocatalytic reactions Collaboration with data scientists and automation specialists to further develop a reliable, fast, material testing process Your Profile
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for data analysis and experiment automation (Python preferred) Excellent English communication skills (written and verbal) Demonstrated ability to work in interdisciplinary and collaborative environments
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properties of the cluster-substrate system using an automated photocatalytic gas test setup for CO2 photoreduction available at A-PECS. Importantly, the PhD candidate will thus receive a joint PhD degree by
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PhD Studentship: Optimisation of Liquid Metal Filtration and Cleanliness in Nickel Based Superalloys
laboratory scale experimentation and characterisation of filtration efficiency across a range of conditions and validate model predictions by developing an automated image analysis tool. To understand
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(such as automated machines and robotics). You will work in this field within a multidisciplinary team of scientists, in strong interactions with stakeholders. Your duties and responsibilities include
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-driven runtime detection-based mitigation. The candidate will design techniques for automated assessment of attack surfaces and vulnerabilities across software/hardware layers.