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Field
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FLOW research group is a young, dynamic group working in the fields of thermodynamics, fluid mechanics, and data-driven modelling. At the Department of engineering Technology (INDI) — Thermo and Fluid
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Description Water can move in two interconnected realms: the fast, visible rivers at the surface and the slower, pressure-driven flow within substrates. Today, engineers can model each realm
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flexibility. To fully unlock this potential, we need advanced tools that digitally replicate these networks and support optimized design and data-driven control strategies. As our PhD candidate, you will
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Development of novel processing techniques Modelling techniques that can inform the direction of experimental activity Physical, mechanical and materials characterisation techniques Data-driven approaches
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
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High motivation and enthusiasm for working in an interdisciplinary research environment Research focus: The successful candidate will conduct research on data-driven modeling of transportation systems
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of electrical engineering, Universite de Moncton (Moncton campus) Email: [mohamed.lamine.faycal.bellaredj@umoncton.ca] Context & Motivation: Because of their extreme computational needs where workloads demand
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(3) years.Supervisor:Dr Mohamed Lamine Faycal BELLAREDJ, Assistant ProfessorDepartment of electrical engineering, Universite de Moncton (Moncton campus)Email: [mohamed.lamine.faycal.bellaredj
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renewable energy, AI-driven engineering, and industrial research. Cranfield’s expertise in wind energy systems, predictive maintenance, and AI applications provides an ideal environment for cutting-edge
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- aware) curtailment tools and the national funded projects Smartlife and Supersized, leveraging model and data-driven digital twins for smart asset management and lifetime optimization of offshore