19 high-performance-computing-"https:"-"CIPMM---Systemic-Neurophysiology"-"https:" PhD positions in Luxembourg
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The University of Luxembourg invites applications for a fully funded doctoral position in mathematical and computational modelling within the framework of the doctoral training unit Forest Function
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high-risk/high-gain research without any teaching obligations. The position is funded through the support of Google.org (more details here ). Our group performs fundamental and applied research in many
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these pNTA results. The person will be employed at the Department of Computer Science and have access to high-performance computing resources suitable for large-scale machine-learning and foundation-model
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datasets Collaborating closely with data providers, clinicians, and technical teams to ensure high-quality data integration, validation, and analysis workflows Supporting documentation, reproducibility, and
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. The group consists of doctoral and post-doctoral researchers from diverse backgrounds. For more information, please visit our website: https://wwwen.uni.lu/snt/research/finatrax/projects Successful candidate
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peptides Perform and support experimental studies across the METAMIC project, including notably metagenomic sequencing of field study samples (from clinical or environmental use cases) Application
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, including participation in seminars, workshops, conferences, and collaborative research initiatives. In addition to conducting high-level research, the doctoral researcher will support the teaching mission
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in electric fleet planning and management, and in integrated transport and energy management systems. This will likely result in publication of at least three high impact factor journal papers in
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infrastructure, and is currently expanding its research activities in exploring several emerging topics of next-generation communications and computing systems. For more details, you may refer to the following
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research program that brings together physics, chemistry, and machine learning. Your research tasks will include: Uncertainty Estimation in Deep Neural Networks for MLFFs Implement and test uncertainty-aware