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architecture, parallel systems, programming language theory, embedded systems or software engineering. Documented ability to teach operating systems and compiler construction. Practical experience of systems
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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their added value against simpler machine-learning baselines; train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies
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activity using fully compressible MHD in global and local frameworks, integrated with physics‑informed machine learning and coronal/wind modelling. Key tasks and responsibilities: Develop and implement data
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of expertise Most candidates will have experience with data science or machine learning, but ultimately, we’re more interested in how you think and learn than what you currently know. PhD or other research
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machine-learning strategies for multimodal representation learning and for the integration of the complex biological datasets generated within the consortium. Duration and start date: 48 months, full-time
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against simpler machine-learning baselines; • train and evaluate ARCA on large-scale microbiome datasets, with attention to sparsity, batch effects, scalability, generalisation across studies and
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, workstation, remote desktop, GPU, driver, performance, identity, storage, and network-related issues, escalating where deeper platform or infrastructure support is required. Integrate Linux desktops and
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The Applied Mathematics and Computational Research Division at Lawrence Berkeley National Laboratory (Berkeley Lab) is seeking a Postdoctoral Researcher – Scientific Machine Learning & Computational
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science, artificial intelligence, or a closely related field. A strong background in machine learning and deep learning. A good understanding of Transformer architectures and modern AI models. Good programming skills