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Field
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Contribute to the development of new algorithms and methods for the efficient analysis of large-scale omics datasets Participate in workflow automation and management using systems such as Snakemake
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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(Edge AI) enables deploying AI algorithms and models directly on edge devices. However, AI workloads demand high performance processing, large scale data handling, and specialized hardware accelerators
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: Translate ML-based error-correction / DPD algorithms into hardware-friendly forms (model reduction, sparsity, quantization, fixed-point design). Design the architecture and RTL of a low-power accelerator that
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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systems. Key Responsibilities Develop graph-based (multi-)omics analysis algorithms Benchmark graph-theoretic against graph-ML approaches Analysis of food-related (multi-)omics data Your Profile The ideal
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therefore paramount, but traditional simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state
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to design adaptive, efficient, and intelligent algorithms for hearing assistive devices. Key objectives include improving speech perception in noisy and unpredictable environments, reducing listening effort
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sophisticated tracking algorithms, AGATA enables precise reconstruction of gamma-ray interaction points, superior Doppler correction, and excellent peak-to-background performance. Working with AGATA provides
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% STFC:25% UoW split), gaining experience in algorithm development, experimental validation, software engineering, and scientific communication. Training opportunities include international workshops