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Field
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and algorithm development, modeling machine learning, and scientific simulation Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists Strong oral and written
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analysis, software and algorithm development, modeling machine learning, and scientific simulation Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists Strong
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on machine learning or classical force fields. 3. Familiarity with open-source coding practices (GitHub/GitLab). More Information Location: Kent Ridge Campus Organization: College of Design and Engineering
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the guidance of artificial intelligence techniques. The project will develop novel design processes that embed material behaviour within agent-based and machine learning computational design systems
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Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill
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from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator). The Project
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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who shares our values and who will support the mission of the university through their work. Qualifications: Ph.D. in a relevant field such as Learning Sciences, Human-Computer Interaction, Education
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optional skills and qualifications: Previous research experience, particularly in the fields of Internet of Things security and machine learning model security applied to intrusion detection. Contracting
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experts in machine learning, classical algorithms, and many-body quantum systems, with research topics including: Adiabatic and measurement-based quantum computing, Tensor networks for machine learning