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PhD at the Forefront of Computational Solid Mechanics and Machine Learning School of Mechanical, Aerospace and Civil Engineering PhD Research Project Self Funded Dr J L Curiel Sosa Application
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Application documents: 1) Brief cover letter, explaining your motivation for applying, 2) Detailed curriculum vitae (including your email address), 3) Complete transcript of grades from all your university-level studies. We do not ask for more information/documents at this point (but you can...
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of the ERC Consolidator project AUTOMATIX (see details below), we are seeking a PhD candidate to develop machine learning approaches for constitutive modeling. Context With the advent of machine-learning (ML
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Error for Off-Shore Wind Measurement Systems Dr Anthony Brooms Software Engineering, Machine Learning, Natural Language Processing, Large Language Models Dr Taolue Chen Static Program analysis and
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cardiovascular MRI, cardiac diffusion tensor imaging, and machine learning or deep learning methods applied to imaging is highly desirable. You will be a collaborative and motivated researcher, capable of working
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confirmed PhD commencement date: To be confirmed Project Key Words: Machine Translation, Natural Language Processing, Terminology Translation, Agentic RAG Post summary We are looking for a candidate for a
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, imbalanced-data, or distribution-shift settings. Job Requirements: PhD in Computer Science, Data Science, or a closely related field. Strong research background in machine learning and artificial intelligence
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The aim is to develop machine-learning models that describe how
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for this PhD proposal should have the following qualifications: M.Sc. degree in electrical engineering, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills
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measurements, airborne measurements from field campaigns (e.g., NASA CAMP2EX, ARCSIX) or surface observations. Research involving machine learning techniques will be strongly encouraged. Field of Science: Earth