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observations like 2D images and transferring those pose/style statuses to 3D characters. By leveraging cutting-edge machine learning models, the project aims to produce accurate estimation of human movements and
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mechanics code capable of modelling arbitrary configurations. The project will investigate the possibility of using AI and machine learning in shear band modelling, developing a deep insight in which
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advanced technological integrations. The selected PhD candidate will have the opportunity to collaborate with prestigious institutions, including the Personal Robotics Lab at Imperial College London and the
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machine learning to CFD-generated datasets. The ideal applicant is a fresh graduate in engineering or in a closely related discipline, with a track record of achievements at the top of their cohorts
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PhD Scholarship: Real-Time Brain Injury Prediction and Protection Framework for Intelligent Vehicles
. The core objectives include: Based on existing car collision simulation and traffic accident database, using machine learning methods to establish the relationship between vehicle kinematics and occupant
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that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from control theory, machine learning, optimization
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' diseased hearts. These models, coupled with machine learning techniques, contribute to the identification of crucial mechanistic relationships and features that offer insights into the trajectory of a
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and/or uncertainty-aware mathematical optimization and control, machine learning and uncertainty quantification with application to intelligent transportation systems and UAV operational planning
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the mobility program of the doctoral network. 2-3 months at Imperial College (United Kingdom). 2-3 months in a partner company (Denmark) Supervision of master students and assistance in teaching activities
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months at Imperial College (United Kingdom). 2-3 months in a partner company (Denmark) Supervision of master students and assistance in teaching activities subject to department needs. Participation in