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to ensure AI models deliver reliable, transparent, and auditable decisions in complex industrial contexts. This project offers an exciting opportunity for you to shape the next generation of industrial AI
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to complex challenges in this field. Essential and Desirable Criteria Solid foundation in computing principles, particularly computer graphics and machine learning. A 1st or 2.1 undergraduate (BEng, BSc, MEng
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background in Computer Science, Mathematics. Students with interests in machine learning, deep learning, AI, uncertainty quantification, probabilistic methods are encouraged to apply. For eligible students
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 2 months ago
: Analyse how curation and drying impact mechanical properties and hygrothermal performance. Develop a Predictive Model: Create a computational model linking operational variables and material properties
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imaging and characterization techniques to mineralized biological materials, with an emphasis on developing correlative workflows tailored to these complex systems. Study 1 will specifically investigate
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for analysing complex materials, structures and model validation. The DIC community has developed guidelines to ensure robust measurements, continually advancing standards through ongoing challenges. In
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Application deadline: 16 June 2025 16:00 GMT Atrial fibrillation (AF) is the most common heart rhythm disorder, but understanding its causes is challenging due to its complexity. It involves
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exploration to enable efficient mapping of unknown environments. Emphasis will be placed on leveraging SatCom connectivity and heterogeneous sensor data and real-time decision-making to adapt to complex
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the integrity of infrastructure such as pipelines and process plants. Traditional inspection and monitoring methods often face limitations when dealing with complex pipework and constrained geometries
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to analyse complex datasets, extract meaningful insights, and guide the optimisation of drug molecules. Collaborate with internal groups, including the Centre for Additive Manufacturing (CfAM) to design and