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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. Furthermore, the postdoc will aid in
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of machine learning (ML) and quantum many-body physics. We are also happy to work with experts in one of the two fields who are committed to learning the other. Moreover, we look for interest in developing
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Research Infrastructure? No Offer Description Job description You will explore hardware/algorithm co-design for NeuroAI, simultaneously taking into account scalability inspired by modern AI workloads, as
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pressure sensors, allowing them to measure the movements of the fish and detect pressure signatures in their wake. Numerical simulations were developed to predict the hydrodynamic signatures generated by
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advantage through coherence-driven materials design and realizing scalable, modular quantum systems. These efforts integrate vertical co-design, from materials to algorithms, and cross-platform co-design
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Foundation RECRUIT grant ("Data Management, Algorithms, & Machine Learning for Emerging Problems in Large Networks – with Interdisciplinary Applications in Life & Health Sciences". NNF22OC0072415
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obtainable using the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. This postdoc position will utilize
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the Oxford Nanopore sequencing platform and improve genome recovery from metagenomes by developing new binning algorithms based on machine learning. The postdoc will be part of the Microbial Metagenomics group
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side, you will investigate and develop data-driven methods for optimal and nonlinear control with a particular focus on dual control and on approaches to measurement scheduling and active sensing
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods