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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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in advanced signal processing techniques and good understanding of emerging machine learning methodologies used in NDE. You will work in close collaboration with project partners at the University
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characterization of samples performing cyclic voltammetry and chronoamperometry. Set up a system under various operating conditions. Carry out experiments to understand mechanisms. Identify components in final
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AI/machine learning, and data analysis using MATLAB or Python. Provide technical assistance on related research projects, such as preparing progress presentations and reports for funding agencies
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to monitor levels of diversity, policies and processes for potential systemic barriers. Although completion of the questionnaire is mandatory and required before review of your application, each question
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invites applicants for four PhD Fellowships in subsurface characterization within geosciences, reservoir engineering, molecular modelling, and machine learning at the Faculty of Science and Technology
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are united by a commitment to excellent teaching, experiential learning opportunities utilising our world class infrastructure and natural laboratories, and a commitment to diversity and respect for difference
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interdisciplinary perspectives. This position offers a unique blend of independent and collaborative research within an interdisciplinary team, with the additional opportunity to teach and mentor undergraduate and
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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to enable higher performance Learns fast, grasps the "essence" and can change course quickly where indicated Raises the bar and is never satisfied with the status quo Creates a learning environment; open to