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the interface of education and social work and ambition to advance one of the aforementioned subject areas Training and experience in qualitative empirical methods/mixed methods research Openness to further
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track record on one of the following research areas: Trustworthy AI AI for formal methods Formal methods for AI The successful candidate will participate in the activities of the research group led by
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an innovative multi-method design, the project integrates: Daily diary and ecological momentary assessment (EMA) approaches In-depth qualitative and immersive fieldwork conducted by the geography team A key
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of the methods. The project is carried out in close collaboration with Helical-AI, an industrial partner specialized in large-scale genomic foundation models and HPC-enabled model deployment, ensuring
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, computational mechanics, computer science, applied mathematics or similar Strong experience with deep learning, e.g. PyTorch, JAX, TensorFlow, and probabilistic methods Familiarity with graph neural networks
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mechanics, applied mathematics, biomedical engineering, computer science or a closely related discipline Strong background in finite-element methods, continuum mechanics and numerical analysis Excellent
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methods; openness to mixed methods research International publications in peer-reviewed journals and / or book publications Demonstrated teaching experience Demonstrated project management skills Ability
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research methods Experience in applied social science statistics is an asset Capacity to work to with interdisciplinary collaborative teams Capacity to work with public stakeholders in the educational field
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methods and educational research A background in GCE or related foci Experience working with primary aged students and / or teachers Fluency in English required, and proficiency in either German or French