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Field
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Engineering or a related field. In this role, the selected candidate will be responsible for designing, developing, and optimising algorithms for processing and analysing signals in real-time applications
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to muon g-2 from lattice Quantum Chromodynamics and algorithmic developments for multi-level and RG-improved simulations (research group of Urs Wenger) C.) Study of multi-hadron systems, with a focus on
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& AI hardware or brain-inspired AI algorithm development, spatial analysis of multi-omics data. We are particularly interested in applicants with a demonstrated track record of translating discoveries
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aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will support the development
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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scalable deployment Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run experiments using the latest ML tools and frameworks
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, files/database structures, data transformation, algorithms, and data output by using appropriate computer language/tools to provide technical solution for application development tasks. Assist with
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computational social choice, and aims to develop formal frameworks and algorithms for eliciting, aggregating, and analysing stakeholder preferences over risk and safety in AI systems. The Research Assistant will
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algorithms and data structures. Experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch). Ability to develop and implement AI models for business applications. Knowledge of cloud computing
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the gap between cutting-edge ML research and practical implementation Collaborate with researchers, developers, and traders to improve existing models and explore new algorithmic approaches Design and run