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Field
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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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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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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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, 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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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
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sequencing (NGS) and omics data analysis. Knowledge of microbial ecology, dysbiosis, and host-microbiome interactions. Familiarity with cell culture techniques. R, Python, or other data science tools
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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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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projects The ability to carry out and publish high-quality research Knowledge on ROS, Python, C++ and Matlab The following qualifications will be considered an advantage when applicants are ranked: Practical
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: Proficiency in Python and major deep learning frameworks such as PyTorch or TensorFlow Familiarity with transformer architectures and large language models (e.g., BERT, GPT) Experience in building, training