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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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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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engineering; Formal methods, models, and languages; Interactive and cognitive systems; Distributed systems, parallel computing, and networks. The successful candidate will work closely with teams specializing
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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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, observational and model-based methods. The third component will establish new partnerships focused on ecology and fire management in several African countries where ecology and fire management are critical topics
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along with complementary structural biology methods. The successful fellow will work at the Institut Laue Langevin (ILL) to develop and apply cutting-edge neutron crystallography approaches