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Field
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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and heat transfer in geothermal systems under high-pressure and high-temperature conditions relevant to AGS. • Developing high-fidelity direct numerical simulation (DNS) models to map
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the areas of fluid dynamics, turbulence and net-zero combustion. There is substantial scope for the student to direct the project with the main focus on (i) Generating an advanced Direct Numerical Simulation
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there are concerns about the new impacts that this will introduce. Battery electric vehicles (BEVs) typically require six times as many minerals as their internal combustion engine (ICE) counterparts, and thus a
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in healthcare service and opportunities for identification of such deviations using computer vision approaches. It will demonstrate how deviation data can be used in computer-based simulation models
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/Simulink/Python for modelling, simulation, and control design. Experience with genset systems, hybrid powertrains, or real-time control applications is highly desirable. A practical interest in system
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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of mechanical and robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the–loop •Background with relevant
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automated feeders), and analyse microbiome composition using 16S rRNA and whole-genome sequencing. Statistical modelling will test for links between microbes and host development and fitness.� PROJECT
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impacts of barrier winds and tip jets in current and future climates via time-slice comparisons from state-of-the-art climate model simulations. Training You will use observations from a series of Norwegian