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that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase retrieval in the weak holographic regime
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. Therefore, we need an imaging scheme that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase
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with computational analysis (Python or R and HPC environments), and a demonstrated, strong interest in genomics and/or polyploidy. Experience in DNA repair assays, cytogenetics, plant functional genetics
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, genetics, plant biology, or a related field, and have documented familiarity with computational analysis (Python or R and HPC environments), and a demonstrated, strong interest in genomics and/or polyploidy
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developments by the project team regarding advanced crystal plasticity models, different modeling strategies will be explored, including strain gradient plasticity [1,8,9], micromorphic approaches [2], and
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developments by the project team regarding advanced crystal plasticity models, different modeling strategies will be explored, including strain gradient plasticity [1,8,9], micromorphic approaches [2], and
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
leverage machine learning techniques to bypass IO bottlenecks in the context of physics simulation on high-performance computing (HPC) clusters. This work is thus placed in a broader ``Machine Learning for
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machine learning libraries. Familiarity with collaborative coding environments (e.g., Git) and working on high-performance computing (HPC) clusters is an advantage. Good scientific writing and communication
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high accuracy in the early stages of next generation manufacturing process, and across many different usecases. As the process matures and enters high volume manufacturing, these models can be
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working in high‑performance computing (HPC) environments, and prior experience with post-training, fine-tuning, or training large language models (LLMs) is an advantage, but not a requirement. Experience