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: https://wasp-sweden.org/ Project description Trustworthy machine learning is an umbrella term that provides methods and tools to ensure that AI and ML systems are verifiable, robust, secure, privacy
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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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networks (CNNs), which identify local correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit
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correlations in the images. However, in this project, the aim is to go beyond standard CNN-based methods by developing new approaches based on transformers, and implicit neural representations (INRs
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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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basic eligibility requirements for third-cycle studies in applied economics. Candidates must demonstrate knowledge of research methods in applied or agricultural and food economics, as well as strong
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are, for example, novel food technologies, but also well-known food processing methods such as for example tofu and tempeh production using raw materials that can be domestically produced. The research will be based
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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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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