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architectures) to large-scale biomedical datasets. These models will be used to work with different types of data from the healthcare and biological domains, including genomic profiles, and clinical event
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, convolutional architectures and surrogate modelling for physical systems Solid understanding of PDE-based models or the motivation to acquire this knowledge Experience with real-time or edge deployment (CUDA
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
understanding genetic architecture). Qualifications and specific competences The postdoc position focuses on developing statistical methods, therefore the candidates must be able to demonstrate they have skills
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will be part of a vibrant research environment at the Interdisciplinary Nanoscience Center. References: [1] A single-stranded architecture for cotranscriptional folding of RNA nanostructures. Science
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environment at the Interdisciplinary Nanoscience Center, where the lab is located. References: [1] A single-stranded architecture for cotranscriptional folding of RNA nanostructures. Science (2014). https
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sequencing, enabling integrative analysis of spatial gene expression, tissue architecture, and genomic alterations at early stages of lung carcinogenesis. Your Tasks Scientific & Computational Responsibilities
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for the post. Examples include: Demonstrated experience in in silico protein analysis, including structural modelling, domain architecture prediction, and identification of signal peptides, processing sites, and
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and adaptive token pruning; Distributed and collaborative inference strategies; Mixture-of-Experts (MoE) architectures for scalable inference; Resource-aware and latency-constrained inference
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requirements quality assurance and architecture analysis, as well as downstream verification & validation activities, such as software testing and runtime verification. For further information, you may refer to
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to explore the dynamic nature of the human replisome, with a focus on replisome protein architecture, replisome speed control, and replication stress responses in cancer cells. By combining CRISPR-based