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Field
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, identify challenges (such as raw material constraints, hydrogen availability, and infrastructure deployment challenges), and analyze deep uncertainties. The research will guide sustainable transition
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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such as model-based optimal control and nonlinear reset control. The goal is to push beyond commercial standards, achieving unprecedented sensitivity by overcoming mechanical and interferometric noise
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large-scale neural models of the early visual system. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience and strong deep learning
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Gallium Nitride: covering the entire chain from device modelling and
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following: soft-robotics, compliant mechanisms, mechanical metamaterials, smart materials or related topics. Capability in computer drawing, modelling and simulating. Proficient in English reading and writing
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on accurate constitutive models that describe the behavior of the molten material during forming. With the increasing demand for more complex components, a step change in model accuracy and associated material
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on the development of new antenna and integrated circuit concepts for radar and electromagnetic modelling. The Electromagnetics (EM) and Integrated Circuit (IC) groups are involved as well as the Center for Wireless
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into the psychological factors that impair consumers' ability to detect such deceptive claims. The project will develop and test a Washing Detection Model to better understand when and why consumers are vulnerable
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models