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theoretical analysis • Structured Learning & Learning Applications
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Engineering, Computer Engineering, HealthTech, MedTech, Product Development, Clinical Engineering or related disciplines with relevant experience in engineering, prototyping, product development or healthcare
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members of SERENE. Person Specification First degree in physics, mathematics, or similar, with a higher degree (or near to completion) relevant to empirical modelling (any discipline), machine learning (any
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and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps
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, and learning platforms including LinkedIn Learning Progressive, considerate leave provisions to empower your work-life balance and well-being, including leading parental leave, gender affirmation leave
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-disciplinary areas of artificial intelligence machine learning big data and data analytics software and security mobility and autonomy The Presidential Postdoctoral Fellowship is proudly supported by generous
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MMA’s program and regional offices, exposing you to a variety of perspectives and issues. Learning Objectives: Under the guidance a mentor, you may gain skills and experience in the following areas: Gain
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Science, Materials Informatics, or a closely related discipline. 3–5 years of postdoctoral research experience with a strong publication record. Demonstrated expertise in machine learning for time-series or multimodal
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In this role you will support the delivery of two exciting, EPSRC-funded research programmes at the University of Nottingham, working across AI, human-computer interaction, and creative technology
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technologies like Azure Data Explorer (ADX), PostgreSQL, MongoDB, and data driven machine learning tools such as Spark, Power BI. Experience with optimisation, simulation and data analysis tools such as Matlab