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causal analysis across distributed datasets while preserving privacy. The successful candidate will be responsible for the end-to-end investigation of novel federated learning strategies for causal
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-scale Logistics. Our vision is that local production, distribution, and reuse of goods using robot swarms will enable a more sustainable future through reduced transport emissions and waste. This vision
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, we are seeking a university assistant to develop advanced machine learning methods for improving the simulation and optimization of distributed systems, for instance by specializing neural ODEs and
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD
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enabling precise and rapid identification of adulteration. Spectral techniques generate unique chemical fingerprints of food items, which machine learning algorithms analyse to detect inconsistencies and
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will contribute to areas such as the design and analysis of algorithms (e.g. randomized, quantum, approximation, property testing, online, streaming, sublinear, fine-grained, distributed/parallel) and/or
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the grant and services delivered by ARC longer-term. The ability to work using one or more of these technologies is therefore essential: Compiled languages (e.g. C/Fortran) Shared and distributed memory
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languages (e.g. C/Fortran) Shared and distributed memory programming tools (e.g. OpenMP, MPI) Accelerator programming (e.g. CUDA, OpenCL, SYCL) Serial and parallel debugging and profiling Parallel numerical
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cell-tracking algorithms, we can follow thousands of individual cells in real time as they respond to carefully designed chemical and mechanical cues. These approaches generate uniquely rich datasets
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geometry and variable stiffness composites Implement bespoke optimization algorithms combining shell geometry and fibre distribution accounting for nonlinearity and uncertainty Conduct optimization studies