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expertise in robotics, sensing, machine learning, biomechanics, and haptics. The project is funded by a NWO VIDI grant to the PI and provides financial security, access to state-of-the-art equipment, travel
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methodology. Experience in large data sets and their platforms/tools, cloud-based architectures, and deployment frameworks for machine learning algorithms. Experience in Deep Learning techniques and solutions
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directions, to comprehensively read literature, to establish appropriate experimental systems, to learn important technologies, and to execute experiments to advance the project and perform data analyses. 2
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in hand by September 1, 2025. Deep knowledge of functional materials and
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immunology, deep learning models and/or artificial intelligence, as well as hands-on experience with cell culture, cellular/molecular biology, and animal studies. The ideal candidate should be self-motivated
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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 1 month ago
methods for image classification including machine learning and deep learning. You will develop clear workflows that allow for regular update of the derived models and maps. Furthermore, you will work
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networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis
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genomic data for reconstructing evolutionary patterns and processes that have shaped biological history across deep timescales. The ideal candidate will have a background in phylogenomics and bioinformatics
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associated with phenotypic (biomechanical and metabolomics) traits. Estimate locus-specific effect sizes and quantifying genetically-driven phenotypic variations. Develop Bayesian models and/or deep learning