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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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the simulation and optimization of distributed systems, for instance by specializing neural ODEs and their training routines. This research will address challenges driven by the energy transition, which is
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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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opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions Exactly: A Bayesian Approach
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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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feedback. The research connects to international initiatives and offers opportunities to collaborate with leading European groups developing open and trustworthy AI systems at scale. Out-of-Distribution
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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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-generation 6G wireless networks. Cell-free massive MIMO represents a significant advancement in wireless communications, where a large number of distributed access points cooperate to serve users without
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is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions. The concept has lately gained increasing interest from
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. The Role This is an exciting opportunity for a highly motivated and skilled Research Associate/Assistant in statistics to join the EPSRC funded project PINCODE: Pooling INference and COmbining Distributions