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evolution across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods and statistical analysis (https://cgrlab.github.io
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to make a difference. Do you want to be involved and contribute to our development? Together, we can create a sustainable future through knowledge and innovation. We believe that knowledge and new
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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media and internet infrastructure computing cultures and materialities as heritage values and economies in algorithmic/data cultures social and cultural perspectives on dismantling communication networks
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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expertise in existing methods and state-of-the-art in the field. The position includes algorithm design, software implementation, and validation on experimental datasets. You will contribute to building a
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application! Work assignments This position focuses on the development of theoretically grounded and practically scalable decentralized learning algorithms under realistic system constraints, including
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Doctoral student in Applied Mathematics with a focus on Computer Vision and Spatial AI (PA2026/1165)
methods, theoretical analysis, algorithm design, planning and execution of experiments, data collection, writing scientific articles, and presenting the results at international conferences. Work duties You
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. Activities include ultrafast quantum physics, quantum technology with rare earth atoms, quantum states in nanosystems, quantum information theory, quantum spectroscopy, quantum algorithms for optimization