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software and advanced computer skills. Demonstrate the ability to learn new techniques quickly and deliver work in a timely manner. Have experience with advanced mass spectrometry platforms, particularly
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including the application of artificial intelligence and machine learning. You will engage with industry, government, and research collaborators, fostering partnerships that deliver outcomes aligned with
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machine learning models with a focus on computational chemistry (e.g. machine-learned force fields) Experience with mechanistic and physical organic chemistry studies of chemical and biochemical systems
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functionalisation, fabrication and characterisation of carbon materials for application in solar cells Predication and discovery of new materials for next generation solar cells driven by machine learning
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for research leadership. Conceptualisation and completion of data analyses with skills related to epidemiology, biostatistics and modelling including machine learning methods A sound understanding of clinical
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machine learning, multi-level complex data engineering, emerging technologies in transport and economics analysis. We’re offering this role as a hybrid position as part of QUT’s commitment to embracing
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modelling, discrete choice modelling, designing choice experiments and machine-learning methods to address sustainable transport and mobility challenges. We’re offering this role as a hybrid position as part
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one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert