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- and wet-lab methods. We combine cutting-edge basic science with strong ties to the clinic and aim to directly influence future clinical practice. The doctoral student project and the duties
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programming Merits: Experience in modelling erosion problems Understanding of critical state soil mechanics, elasto-plastic and elasto-viscoplastic models Experience in numerical analyses (using finite elements
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you interested developing new machine learning methods for precision medicine and
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tailored materials design with advanced characterization methods to enable new device functionalities. The research aims to expand the capabilities of organic electronic devices by integrating light
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parallel to what has recently been shown in human gut health, microbial diversity on both leaves and roots is an important factor in overall plant health. However, the connections between plant disease and
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Trustworthy machine learning is an umbrella term that provides methods and tools to ensure that AI and ML systems are verifiable, robust, secure, privacy-preserving, and ethical, which leads to greater adoption
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leaves and roots is an important factor in overall plant health. However, the connections between plant disease and plant-associated microbial communities are not well known. Prevalent plant pathology
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own research question within this area. The project is mainly intended for quantitative methods but is to some extent also open to qualitative methods. As a doctoral student, you will read doctoral
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the electronic properties of organic semiconductors through light-driven chemical doping. The work will involve combining tailored materials design with advanced characterization methods to enable new device
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develops an adaptive AI-guided XR platform for capturing and transferring expert manufacturing knowledge. Your focus will be on developing AI methods for analyzing and modeling human workflows based on data