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Meritorious will be considered experience in: - Microscopy-based imaging - Immunohistochemistry - Work with clonal cell lines - Work with other model organisms such as D. melanogaster or D. rerio You are a
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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analysis, modelling and field experiments, our project will illuminate the way forward for assisted migration (AM) as a pathway to sustain, and to restore in case of degradation, the biodiversity and
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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-studies/. Background and description of tasks PhD project 1: The PhD project involves research using invertebrate model systems to investigate the mechanisms by which potential host genome editing processes
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robots -Distributed task planning for collaborative missions -Behaviour tree based multi agent collaboration -Reactive task allocation in large scale missions For further information about a specific
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methods for optimized data analysis, Machine learning-based image segmentation of tomographic data (e.g., synchrotron X-ray microtomography), Design and use of autoencoders (VAEs, GANs), diffusion models
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basic understanding of, and a strong interest in, one of the following topics: Logic-based reasoning approaches, for example formal argumentation, or Formal aspects of autonomous agents and multi-agent
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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
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, particularly related to data sharing between actors in the supply chain. This project will study how digital product passports (DPP), and ontology-based platforms for collecting and interpreting such data, can