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inversion techniques and signal processing. Strong programming skills, Proficiency in scientific computing (e.g. Python, MATLAB, or similar) for algorithm development and data handling. Experience with sensor
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/ Digital forensics; Algorithms. Supervise final year projects and capstone projects, provide mentorship and academic guidance to students; Collaborate with colleagues to develop or enhance the curriculum
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in a research project on machine learning modeling and optimization of vertical farms. Key Responsibilities: Development of new machine learning modelling approaches Development of new advanced control
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equivalent). 3+ years of professional experience in software development or research, with a focus on data structures and algorithms. Teaching experience at the college or university level in data structures
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combination of statistical, quantitative and qualitative methods. Extracts, manipulates, and interprets data using statistical software (E.g., SPSS, R) and SQL-based query applications (e.g., Cognos) Develops
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binding pockets. About the role We are seeking a highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to identify domain functional families
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supervisor and research team. Duties can include: Conduct research to resolve complex technical issues, Develop theories and models to address computing problems Design, develop, and test software, hardware
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perceptual and cognitive tasks, investigating the generality of behavioural biases. A recently-developed statistical sampling model of human cognition will then be compared against existing approaches with
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Estimation. The research includes the following activities: • Develop algorithms and research directions to improve state estimation of polar ice caps (Greenland/Antarctica) • Calibration/Validation of high
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University you will develop, optimise, and oversee our analytics data architecture, tools, and pipelines. You will engineer solutions that allow for the efficient and reliable handling of large scales of data