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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
macromolecular dynamics, as well as collaborations with the European MDDB initiative. The candidate will develop new graph transformer architectures to learn conformational heterogeneity from molecular dynamics
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plasticity platform. Different machine learning strategies will be explored to capture the complex relationships between microstructural features and mechanical responses. In particular, the project will
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performance. This PhD project aims to develop a data-driven framework for graphene aerogel design by integrating structured experimental Design of Experiments (DoE) with machine learning (ML). The student will
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system design? We
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this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models
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: The impact of construction waste management system design on waste sorting performance, with the aim of reducing unsorted and abandoned waste The potential of incorporating human behavioral factors into system
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department At HTSR , interdisciplinary expertise is brought together to aid decision-making on health innovation development, evaluation, optimization, and implementation. Research concentrates on quantitative
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the experimental foundation and enables quantitative evaluation. Representation learning with physics-guided data augmentation and adaptation: Design of self-supervised learning strategies to learn shared and
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics