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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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background in geomodelling, geophysical and geotechnical investigation, geomechanical engineering, and machine learning. You will be expected to work effectively on a geophysical/geomechanical project, to
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research in machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to any
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record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, or optimization Proficient in both written and spoken
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geophysical investigation, geomechanical engineering, and machine learning. The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space
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. To collaborate with industrial and academic partners. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics
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, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model-predictive control, etc
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theories in inverse/scattering problems. Once successful, the candidate is expected to transfer their expertise to machine learning/scientific computing in collaborations with other group members (phd
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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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Computer Science, Artificial Intelligence, Mathematics, Engineering, or a related field. Entry level candidates with demonstrated expertise in artificial intelligence (AI), machine learning, deep learning