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Field
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Learning and Quantum Optimization (ICMM, Madrid) Develop theoretical frameworks for qudit-based quantum neural networks and quantum active learning (QAL). Investigate hybrid quantum-classical pipelines
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for internal and external projects, bringing it up to industry standards. You will Develop and optimize the specificity assay for biologics, with technical support. Identify and assess innovative applications
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, Optimization, and AI • ML/AI for mobility prediction and optimization • Graph algorithms, network science • Spatiotemporal modeling • Operational research for mobility and infrastructure • Real-World Practice
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foundation. We are looking for a candidate excited by the challenge of rethinking data systems in the face of modern constraints such as optimization under uncertainty, constrained query evaluation
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and Trust (SnT) at the University of Luxembourg is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in
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-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
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. This unique nascent university, with its state-of-the-art campus and infrastructure, has woven a sound academic and research network, and its recruitment process is seeking high quality academics and
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-effective use of network signaling. The main idea is to explore new optimization approaches and robust techniques to handle channels and other parameter uncertainties and temporal variability, along with
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. These insights will then be integrated with genomic large language models to design optimized regulatory patterns for modern maize, which will be validated in collaboration with a nationwide team. Modeling
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(Python required; R, C++ appreciated). Strong interest in network modeling, multi-criteria optimization, or graph theory. Skills in statistics and data analysis. Experience in 3D modeling, phenotyping