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to learn unfamiliar tools as needed (e.g. GitHub, APIs, new libraries). Familiarity with econometric techniques, especially panel data methods. Clear, concise written and verbal communication skills. Ability
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and written) in English Proficient in Microsoft Office Software (Microsoft Word, Excel, PowerPoint) Hardworking, proactive, willing to learn new things Ability to work both independently and as part of
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progression to PhD depending on performance during the initial period. Main duties and responsibilities Admin - ordering consumables, tracking cost and use of consumables PIL training - opportunity to acquire a
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, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will have experience in one or more of these subject
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One Research Associate position exists in the data-driven mechanics Laboratory at the Department of Engineering. The role is to set up a machine learning framework to predict the plastic behaviour
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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, and digital approaches using mutual learning, inclusive theory-development and creative methodologies (e.g. filmmaking, VR technologies) The successful candidate will support the Co-Investigator in co
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techniques from optimization and control theory, scientific machine learning, and partial differential equations to create a new approach for data-driven analysis of fluid flows. The successful applicant will
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our team but also domestically and internationally. A successful candidate will be open to learning new research areas in the collaborations. With a high research performance, there are potential
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novel sensing approaches to combine with machine learning algorithms to solve real-world problems in food manufacturing. You will have sound knowledge in electronic engineering, embedded systems design