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Field
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the manufacturing and characterization of material samples. As such, the position offers the opportunity to be involved in fruitful national collaborations. In this exciting job you can expect: to develop automated
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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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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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prior experience in at least three of the following areas: Python programming Develop LLM-based tools to automate data connector generation for data ingestion. Design and implement a multi-layered storage
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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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includes projects in biomedical instrumentation, experimental hardware automation and programming, and computational science. Scientific publications in a relevant area are considered a significant benefit
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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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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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-driven runtime detection-based mitigation. The candidate will design techniques for automated assessment of attack surfaces and vulnerabilities across software/hardware layers.
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. The acquired and already existing database will be used to further develop ML models for the automated detection of clinically relevant markers. The goal is to develop the technique towards potential clinical