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-disciplinary research environment Desirable criteria 1. Experience in devising and developing novel machine learning algorithms 2. Hands on experience with ROS and physical robots 3. Excellent
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Applications are invited for one full-time EPSRC Industrial CASE (ICASE) PhD studentship for each project. “Development of natural-ageing-resistant, heat-treatable lean aluminium alloys
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will be tailored to your expertise, spanning from hardware design to system-level optimization and control methods. For the AI position, you will develop machine learning models that incorporate physical
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to apply it in selected poor-resource settings. This project aims to achieve several objectives, including the development of a new AI-algorithm and a paired dataset for comparing how different imaging
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. Philipp Petersen, M.Sc.. The research areas developed by the team are in particular related to theoretical analysis of classical problems in numerical analysis in the framework of modern algorithms
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consists of three major parts: sensor printing, circuit design and integration and developing of an AI algorithm and using it to teach the sensor to selectively measure desired gases . In this role, you will
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(including a Parenting and Carers Fund and the Carer’s Career Development Fun d), training, and a variety of diversity and inclusion networks. Staff can apply for flexible working to help them balance
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developing mathematical algorithms and simulations in MATLAB, in particular with Semidefinite Programming and Sum of Squares and of the analysis and design of feedback control systems using these approaches
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multidisciplinary R&D team and leading scientists from Oxford and Stanford, contributing to the development of scientific research, you will play a pivotal role in shaping the future of fatigue detection and
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, and maintaining software systems to support these research projects. This includes building data pipelines, developing algorithms, and ensuring that data is stored efficiently and is accessible to all