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working with large collaborative code projects Proficiency in applying AI-driven algorithms (e.g., neural networks, reinforcement learning) for the creation of surrogate models and the autonomous
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algorithms into efficient edge inference systems, and validate end-to-end performance under real-world deployment conditions. Throughout, you will work with live measurement data spanning the full arc from
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, the Neutral Atoms team, the Spin qubit team, the Intra- and Inter-connects team and leads within the Applications and Algorithms team. Qualifications We imagine that you are assistant professor and that you
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designing DNA, RNA and proteins to create nanoscale devices for applications in biotechnology and medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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to algorithms with actionable performance guarantees. More specifically, the research will revolve around the following theme: High probability convergence in stochastic optimization under heavy-tailed noise
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-loss events undermine statistical confidence. The aim is to develop i) edge intelligence (on-turbine smart algorithms for data preprocessing), ii) resilient data movement (error-tolerant, cybersecure
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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. Experience with phase retrieval algorithms, clean room use and e-beam lithography are beneficial. The candidate will be expected to participate at international user facilities and thus will be expected