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motivated to move the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine
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patterns across multiple annotation types. The core aim is to generate new scientific insight by associating LCRs with their functions through a combination of expert curation and modern machine learning
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About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside
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geometry altogether and operate in hyperbolic space. Our lab has published multiple papers showing that hyperbolic deep learning has strong potential for computer vision, from hyperbolic image segmentation
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systems strong analytical and problem-solving skills fluency in English, both written and spoken Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications
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, their achievements and productivity to the success of the whole institution. The Faculty of Computer Science, Institute of Theoretical Computer Science, the newly established Chair of Algorithmic and Structural Graph
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Not required, but helpful: Experience with biomedical data/algorithms An affinity for applications of technology Contributions to open source projects This is what we offer A temporary contract for 38
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works have shown that for embedding hierarchies, we should abandon Euclidean geometry altogether and operate in hyperbolic space [1]. Our lab has published multiple papers showing that hyperbolic deep
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GPR and EMI imaging methods at multiple scales to enhance our understanding of the soil–root system Designing and implementing novel inversion algorithms for GPR and EMI data Identifying links between
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(rhizotron facility) and field trials. In addition to field applications, novel inversion algorithms for ground-penetrating radar (GPR) and electromagnetic (EM) will be developed. These algorithms will enable