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Field
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seeks to enhance the predictivity, accuracy and applicability of FEA for WA-DED, enabling more efficient design and control of large-scale additive manufacturing processes. The student will be based
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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control over bacterial cellulose (BC) production, aiming for modifications, enhancements, and customization through a streamlined process. This is a The RA will focus on integrating synthetic biology and
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research methodology and present research findings in a structured way Ability to work independently and as part of a team Flexibility, willingness to learn and travel Excellent command of written and spoken
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treatment processes through advanced machine learning, validated against physics-based models and experimental data. System Integration: Integrating the DTs into material and energy balance equations
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Grid Solutions Ltd on behalf of GE Vernova. The project’s topic will revolve around advanced high-voltage power electronics design and control, addressing both academic and industry needs. HVDC
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that provides timely and effective support to real-life policy processes. The successful candidate will become part of an interdisciplinary team and support the development of data and model assets which are used
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attention on food safety, climate-resilient agriculture, and regulatory controls, accurate detection and risk assessment of such mycotoxins have become critical components of modern food science, toxicology
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metrics during both standard operation (primarily governed by system reliability) and extreme events (primarily governed by robustness and restoration). This will be achieved by building on previous
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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive