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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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-atomic potentials using a combination of classical and machine-learning (ML) approaches (and a new hybrid method recently developed in our group). Some of the types of simulations that will be performed
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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and standing recognised by the University/profession as appropriate for the relevant discipline area (e.g., AI/Machine Learning, Bioinformatics). A proven track record of research and scholarly
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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collaboratively with colleagues from multidisciplinary disciplines Excellent time management and planning skills, with a commitment to delivery Strong background in machine learning and/or deep learning, and signal
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independently and as part of an interdisciplinary and cross-cultural team, possess excellent organisational and communication skills, and work collegially with other staff. Advanced computer skills, including
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computer skills with experience using Microsoft Word, Excel and PowerPoint; and specific experience in working with a range of data management/analytical software. Prior experience, knowledge and expertise
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(PhD entry Level - $108,156 p.a.) Join a collaborative and cutting-edge research environment working with world-class researchers. Apply statistics, bioinformatics, and machine learning methods
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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