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Field
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Deadline extended: 30 October 2025 (originally 29 September 2025) A fully funded 3-year PhD is available in the School of Computer Science and Engineering (SCSE), Bangor University. The project will
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the investigation and realization of improved microwave probe design, data processing, and visualization techniques to provide a robust method of data analysis, flaw characterization and sizing. AI/machine learning
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with expertise in digital signal processing methods, and machine learning methods for amplitude and phase noise characterization of optical frequency combs, recovery of dual-comb measurement signals and
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on the performance of the CMF; Using machine-learning (deep learning) methods to develop a predictive model and conduct the sensitivity study to investigate the multiple factors on the performance of flow meter
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highly motivated and ambitious PhD candidate with experience in either biomedical engineering, machine learning, polymer technology, physics, electrospinning, or similar fields,to join our Lab- on-a-chip
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Would you like to work at the intersection of transportation, robotics and machine learning
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learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts. For example, we would like to be able
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intelligence, NLP, machine learning, or a related field Experience with Python and Generative AI libraries (e.g., Huggingface Transformers) Knowledge of Multimodal Generative AI models and their corresponding
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and Computer Vision Excellent programmer in Java / C / Python or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical