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postdoctoral experience). Proficient coding skills in at least one of Matlab, R and Python. Experience and proficient in processing different types of remote sensing datasets. Experience with terrestrial
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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physiology Coding proficiency (e.g., Python, R) and familiarity with deep learning tools are strong assets A strong publication record, critical thinking, and collaborative mindset Application Instructions
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physics, and quantum error mitigation. Good programming skills in Python. Familiarity with PyTorch or tensorflow will be a bonus. Proficiency of programming skill with quantum machine learning architectures
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qualitative and quantitative data analysis. Proficiency in programming (e.g., JavaScript, Python, React for iOS/Android) is important. Additional strengths would include experience with physiological sensing
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relevant programming languages (e.g., Python, MATLAB, R) is a requirement. Familiarity with downscaling and bias correction of climate data (e.g., from CMIP/PMIP) is an advantage. Experience with
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experience in python, C++ or other relevant language and experience in deep neural networks Strong mathematical capability, especially for Theme 1, with applicants holding an undergraduate or postgraduate
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, or related field. Proficient in MATLAB and/or Python. Experience with image processing and rectification. Strong understanding of coastal and ocean wave physics. Application Requirements A complete
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systems, MARL, Game Theory. Strong background in Mathematics and modelling. Good English writing and communication skills for research work. Proficiency in Python, experience with PyTorch, NumPy, and
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one area relevant to prototyping playful experiences (e.g., programming languages like Python/JavaScript for interactive web/app development, game engines like Unity/Unreal, physical computing platforms