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Field
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range of expertise in various archaeological science fields such as Bioarchaeology, Material Culture Studies, and Digital/Computational Archaeology. What you bring (selection criteria) In this team, we
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relevant field, such as Food Safety, Data Science, Food Science, Biotechnology; experience in predictive modelling and quantitative microbial risk assessment approaches; experience with relevant software
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candidate with a doctorate in computer science, mathematics, data science, or related field with relevant experience. A strong interest in clinical applications is essential. Candidates who expect to obtain
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item
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Master’s degree in Data Science, Artificial Intelligence, Computational Linguistics, Computer Science. Has excellent academic writing and oral skills in English. Has experience with large language models
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the computational imaging group at CWI, where you will have a chance to interact and collaborate with fellow PhD-students and research staff with a background in mathematics, computer science, and imaging science. It
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prototypes of novel gear for the detection of flatfish. Publishing your findings in scientific journals, integrating research from (bio)mechanics, and fisheries perspectives. Communicating about the project
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development. You will also apply a coastal dune model (AeoLiS) to dunes in the Netherlands and evaluate how well it replicates historical changes. You will share your results in stakeholder meetings, scientific
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scientific programming (e.g. Python, MATLAB, or R) for data analysis and visualization A clear affinity for insect behavior, sensory ecology, or movement ecology; Fieldwork experience or a strong interest in
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publish it as your PhD thesis. Your team Your WUR supervisors will be Dr. Paul Smeets and Prof. Dr. Ciarán Forde (Sensory Science and Eating Behaviour chair group). Dr. Davide Risso, external research