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Field
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PhD Research Fellow in ML-assisted reservoir characterization/modelling for CO2 storage (ref 290702)
, preferably in seal/reservoir/overburden heterogeneities, static and dynamic reservoir modelling, and flow simulation. The candidate will work in a team of geologists, geophysicists, geochemists and staff with
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the state of the art in emerging wireless networks, including simulation aspects; - collaborate in the preparation of technical reports on the algorithms, mechanisms, models, or protocols developed
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Job Description Job Alerts Link Apply now Job Title: Research Fellow (Urban climate modelling) Posting Start Date: 20/11/2025 Job Description: Job Description We are seeking a highly motivated and
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System and beyond. The new climate model, OASIS, aims to overcome the limitations of current frameworks and enable unprecedented simulations of planetary environments under a wide range of conditions. The
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degree in Mechanical or Vehicle Engineering or the related fields 3-year research experience in combustion, emission, or thermofluid modelling. Expertise in numerical simulation methods, particularly
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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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of an optimisation-based simulator to assess how grid-edge devices contribute to power grid flexibility. You will use advanced modelling and simulation tools such as PandaPower and OpenDSS to evaluate the impact of
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modern control theory (PID, adaptive, robust, and fractional control); Experience with fuzzy logic and AI-based control (reinforcement learning, neuro-fuzzy systems); Skills in modelling and simulation
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modes, effects, and criticality requires deep domain knowledge and careful analysis. Collecting High-Quality Sensor Data. Simulating Realistic Fault Conditions. Developing Reliable Fault Prediction Models
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infectious aerosols in buildings (ASHRAE Standard 241). The work involves analysis, simulation, and integration of real-world data into risk models that directly inform how buildings are designed and operated