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utilizing modern programming tools and languages tailored to the project's requirements (e.g., Python, JavaScript, Django, among others). The activities may also include testing and validation on both
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
industrial decarbonisation and/or East Asia will be highly regarded, as will proficiency in Python, Julia, or RStats, and experience in industry engagement. To excel in this role, you will be a self-starter
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motivated Postdoctoral Fellow with strong expertise in RNA-seq and multi-omics data analysis. Candidates should be proficient in Python and/or R and have a solid background in Biology, Computational Biology
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datasets using Python or R. Ability to work both independently and collaboratively, with strong organisation, attention to detail and communication skills. Clear motivation to apply interdisciplinary and
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meteorological datasets. Good programming skills in languages such as Python, MATLAB or R. Familiarity with UNIX/LINUX. High level analytical capability. Ability to communicate complex information clearly. Ability
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Python, ML frameworks such as TensorFlow or PyTorch, statistical analysis, and strong IT skills are essential. You will thrive under tight deadlines, handling sensitive data with confidentiality, and have
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, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python, or embedded software tools. Integrate controller logic with
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collection. Can demonstrate proficiency in R or Python of data handling and visualisation. Has understanding of marine food web dynamics and trophic models alongside strong quantitative and analytical skills
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health, behavioural, or social science datasets. Proficiency in Python and/or R, and familiarity with open access AI/ML libraries. Track record (or strong potential) of peer-reviewed publications
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libraries (e.g. Python, TensorFlow/PyTorch, Scikit-learn) Demonstrated experience in applying machine learning or data-driven methods to physical systems, preferably in the context of fusion research Proven