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Field
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using both physical and machine learning-based algorithms. Strong interpersonal and communication skills and the ability to work both independently and collaboratively with researchers and students from
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, computer vision and machine learning algorithms. · Information dissemination and decision-support services · Policy related analysis and investigation · Previous interactions with transportation funding
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing multidimensional networks by studying the theoretical foundations behind randomized
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signal-to noise Post-processing: denoising, reconstruction algorithms Comparison with high-field MRI: deep-learning and other AI modalities for low-field MRI optimization Close cooperation with
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these autonomy and self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed
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techniques. Familiarity with spatial transcriptomic technologies and biological data interpretation is a plus. Familiarity with optimizing cell segmentation algorithms for enhanced accuracy and efficiency, as
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We offer PostDoc positions in the area of Quantum Software Verification, Compilation and Optimization. Interested applicants with strong analytical skills and a desire to work on algorithmic and
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research in machine learning (ML) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time
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methodology will involve the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation