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them for specific tasks. The project will combine: Mathematical modelling of dynamical systems; Computational photonics simulations; Comparison with real physical systems (especially photonic systems
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will use a high-fidelity large eddy simulation (LES) code and scientific machine learning tools, such as real-time optimisers, in order to simulate wind farms exposed to various atmospheric inflows. Some
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, fluid flow, and solute transport. Develop efficient computational coupling strategies to integrate these physical processes. Apply advanced numerical methods to construct dendrite growth models, including
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the scope of the provisions of the collective bargaining agreement. Your qualifications Must-haves A Master’s degree (completed or near completion) in Computer Science, Machine Learning, Mathematics, Physics
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. Desirable: Experience in multi-physics simulation tools (ANSYS, COMSOL), high-speed PCB design, embedded DSP/FPGA control, or other related skills How to apply: Interested candidates should submit a full
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honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline. Applicants should also demonstrate evidence of
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in Process Industries; Net Zero (PINZ’) as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A ‘Personal Statement’ (this is a mandatory
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Area Engineering Location UK Other Closing Date Thursday 30 April 2026 Supervisors: Dr Yaoyao Zheng , Prof. Hao Liu , Dr Omid Saghafifar (Remedium ) Programme Length: Four years Contract Type: Full
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Area Engineering Location UK Other Closing Date Thursday 30 April 2026 Supervisors: Dr Ming Li , Prof. Hao Liu Programme Length: Four years Contract Type: Full-time Prospective Start Date: October
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‘Course Search’ to identify your programme of study: Search for the ‘Course Title’ using the programme code: 8856F Leave the 'Research Area' field blank Select ‘PhD in Process Industries; Net Zero (PINZ