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Bayesian inference, likelihood-free inference, uncertainty quantification, identifiability analysis, or scientific machine learning. Strong programming skills (Python, Julia, Matlab, C++, or similar). Strong
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Strong programming expertise in Python and experience with frameworks such as PyTorch, Hugging Face Transformers, or equivalent tools Demonstrated ability to develop scalable, automated, and reproducible
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techniques. ● Proficiency in instrument control, data acquisition, and data analysis using tools such as LabView, Python or MATLAB. ● A record of peer-reviewed publications and demonstrated ability
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(for example Python, bash, or an equivalent language). ● At least one peer-reviewed publication in a scientific journal. Preferred Qualifications: ● Experience with free-energy or phase-boundary
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. Python programming, or a willingness to learn. Higher degree student supervision. Strong initiative, resourcefulness and the ability to work effectively to deadlines. About UQ As part of the UQ community
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of experience within the field. Scientific and numerical programming in Python or R and knowledge of class ML methods and deep learning methods. Well versed in a variety of bioinformatic analyses, including Next
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, computational biology or a related subject area. Experience in human genomics and a good understanding of cancer genomics are required. The candidate should have proficiency in R, python, or comparable
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spintronics, or cavity spintronics. Proficiency in scientific software development (e.g., Python, COMSOL, HFSS). Demonstrated ability to work independently and collaboratively in a multidisciplinary environment
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, or related biodiversity forecasting approaches; advanced programming skills in R and/or Python and experience with reproducible analytical workflows; experience handling large geospatial, biodiversity
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, quantitative data analysis, and simulation using tools such as Python, LabVIEW, MATLAB, Mathematica, Origin, or COMSOL Multiphysics. Background in superconductivity, magnetism, thin films, surfaces and