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testing of contemporary and vintage runner materials will be performed to assess the influence of ageing, microstructural variability and service history. A purpose-designed model specimen will be developed
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thinking about how novel AI tools might help or hinder the research process. In the long term, the research will also help inform the design of novel ERAs. The PhD candidate will be based at the Egenis
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Manufacturing is one of eight major themes at Cranfield University. The manufacturing capability is world leading and combines a multi-disciplinary approach that integrates design, technology and management
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environment used in APEX (sectoral simulators and/or their ML metamodels), with support from the team. Formulate the policy-learning problem, including state/action representations, reward design (with
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 22 days ago
3D and image-based computer vision Experience implementing 3D computer vision algorithms and training end-to-end machine learning models (from data collection and design to evaluation) Knowledge and
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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of electrochemistry. Your tasks The student will be involved in tasks such as Designing innovative electrode materials with precisely engineered nanostructures; Fabricating the electrode materials using micro
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in compliance with all institutional and federal regulations. Design and execute experiments using relevant in vitro and in vivo cancer models. Develop new research ideas, therapeutic approaches, and
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will design “smart” aqueous or gel electrolytes and matched cathode coatings that work as a single system to: control how Zn2+ is solvated and transported, suppress dendrites and cathode dissolution, and
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to generic design approaches that overlook plant ecology. This PhD project will transform rain garden design by linking plant traits to hydrological and ecological outcomes, ensuring resilience under