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research, teaching, and promoting an environment that allows all members of the department to thrive. Principal concentrations include weather prediction, air quality, air-sea interactions, climate modeling
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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methodologies, advanced controller synthesis, performance and stability assessment. Trade-off, prototyping and selection of advanced DFAOCS control methodologies: minimum set to be explored: model predictive
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test coupons used in HiSOPE RF/microwave PCB & interconnects: Layout controlled‑impedance CPW/microstrip transitions from drivers to OLED fixtures; model launch structures, vias, and ground‑reference
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. However, in many real-world and latency-critical applications, performance cannot be assessed solely through final recognition accuracy. Instead, the value of a prediction strongly depends on its timeliness
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acousto-structural transmission paths, developing predictive models, and producing research outputs that support practical noise mitigation solutions for the built environment industry. Key Responsibilities
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Application deadline: All year round Research theme: Environmental geochemistry How to apply: https://uom.link/pgr-apply-2425 This 3.5-year PhD studentship is open to EU, UK, and US applicants. The
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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. Experience with advanced analytics, including predictive modeling, data science, or statistical analysis to support data-driven decision-making. Demonstrated experience designing and implementing ETL/ELT