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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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matter gravity simulators, enabling the identification of exotic properties of relativistic materials. Use of various theoretical methods to study the effects of field theory properties in curved spacetime
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that its amplitude can reach 10-20% of the DC value. Despite numerous studies on the physics of RTN, there are still many unanswered questions. The type of defects, their location in gate oxide but
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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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, 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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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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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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on extending methods for detecting the pose of an object (possibly occluded, even if only partially) held by a person to 360-degree robot vision, in line with mesh detection and biomechanical variables
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for a duration of six years. NumPEx contributes to the design and the development of numerical methods, software components and tools that support future productive European exascale and post-exascale