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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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, image processing, biological modelling and biostatistics. Experience working with (or knowledge in) plant cell walls, phytohormones signalling, mechanobiology, plant growth and development. Experience
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of teaching and learning (methods and models) as well as its actors (teachers and pupils/children) and their relationships. The research school is linked to a training environment for teacher education, where
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educational contexts, and how content is legitimised. Furthermore, scientific didactics investigates the forms of teaching and learning (methods and models) as well as its actors (teachers and pupils/children
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modeling of magnetic materials using first-principles methods. Good knowledge of programming is required. Meritorious experience for the position is demonstrated knowledge of Git, Python, Bash and VASP. Good
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qualifications and merits for the position are: • Knowledge and experience on image processing or computer vision • Knowledge and experience on generative AI • Knowledge of data driven methods for modelling and
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for carbohydrate, lipid or protein analyses. Skills in computational biology or biological modelling. Experience working with cell walls from plants or other organisms. Be proactive and take own initiative
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. The applicant should have strong background in mathematical foundations of computer science and experience in Python programming. Previous experience in deep learning, reinforcement learning, or explainable AI is