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driving through deep reinforcement learning. Computing demands can grow rapidly with such models, so a significant aspect of the research is in formulating the problem in a tractable form, and application
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detect changes in step quality associated with acquired brain injury?” and involves a programme of laboratory-based work using a smart pressure-sensor based insole and app. We aim to develop biomechanical
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Adopter programmes, where appropriate. We are looking for an enthusiastic, motivated graduate in (health) Data Science, Computer Science or related field who is interested in the deployment, user acceptance
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global castings industry. The AMRC Castings Group is a leader in advancing casting technologies and techniques. Our team provides advanced casting expertise, including computer process modelling, design
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Improving Deep Reinforcement Learning through Interactive Human Feedback School of Computer Science PhD Research Project Directly Funded Students Worldwide Dr Bei Peng, Dr Robert Loftin Application
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contexts. The project will build on both consolidated knowledge in the history of technologies, and the most recent literature on the perception of digital and computer-assisted creative outputs
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, reactor design and computer programming. Entry requirements: Upper second class degree in Chemical Engineering, Materials Science and Engineering or Chemistry. Funding Notes Department contribution (fee
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together with a dynamic and friendly team of researchers across two different laboratories including cell biologists, biophysicists and computational experts. This project will give you the opportunity
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, has been shown to have a synergistic effect in increasing the toughness of composites in some cases. This research is following the recently started research program on understanding the effect of nano
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-resolution imaging and reconstruction of neural tissues (see https://ist.ac.at/en/research/siegert-group/). Leveraging computational tools such as machine learning and topological data analysis, we will