137 software-engineering-model-driven-engineering-phd-position PhD positions at RMIT University
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This PhD scholarship is for candidates interested in developing culturally inclusive AI by creating methods and datasets to align large language models with global social and legal norms. This PhD
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via RMIT University application portal. Apply via RMIT University application portal. Potential candidates must have a good hold of distributed software systems, machine learning and cybersecurity
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. There is a high likelihood of continued employment with a competitive numeration at APV Engineering, after the completion of PhD. This is a co-funded highly competitive scholarship, only available
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scales), chemistry, engineering physics, nanotechnology. Experience or strong interest in modelling, and/or clean energy. Preferable background and/or previous experience or publications in materials
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An industry-linked PhD project with very competitive scholarship, open to Australian candidates, with possibility of post-doctoral employment at a globally recognised company for innovation and
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in biomedical engineering, mechanical engineering, or a related field. Experience in finite element modelling, biomechanics, or orthopaedic research is desirable. Strong analytical and problem-solving
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electronic academic transcripts A CV that includes any publications/awards and the contact details of two referees. Please put “PhD Scholarship Application” in the subject line. For international applicants
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From smart cities, data-driven health and social services, to digital economy, safety and security and digital government, Data61 create data you can trust. Data61 PhD Scholarships and Data61 Top-Up
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Join our multidisciplinary research team to develop and apply machine learning and bioinformatic algorithms in biomedical research. This PhD project will focus on developing machine learning
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@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent