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Individual differences in inner speech: Measurement and modelling provided the necessary funding is available. This PhD fellowship is available as of 1 February 2027 for a period of up to three years (5+3). It
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measures that are robust, reproducible and relevant to assessments used in patients and mammalian disease models. Your research will combine different approaches to: Develop and standardise behavioural
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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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and functional materials. UV photopolymerisation, and more recently LED technologies, enable rapid, localised and energy-efficient processes. Recent developments in UV LEDs at different wavelengths, as
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Develop advanced models to understand and predict piping
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at the HOME RATE ONLY. International students and EU students without Settled Status will need to cover the difference between the home and the international fee rates. Visas and associated costs are not
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particles and flowing water across different spatial and temporal scales, while keeping the models computationally efficient. The research builds on an Eulerian two-phase modelling approach previously applied
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around the modelling protocol: diagnostic checks of scenario submissions against the protocol, and documentation of harmonized assumptions and remaining differences across models Contribute to / develop
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LiDAR based foundation models to radar, as well as the development of multimodal foundation models incorporating radar. A further challenge is how different radar representations and sensor configurations
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of treatments for a specific disease or condition can vary across individuals, so that in settings where multiple treatment options are available, different individuals may require different treatments to obtain