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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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, conduct experiment, and deploy Large Language Models on high-performance cloud Linux servers; and (f) assist in summarising and writing up research findings. Qualifications Applicants should: (a
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or clinical datasets. Proficiency in Python and R, with strong experience in Linux/HPC environments and workflow automation. Track record of publications in high-impact journals and contributions to competitive
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learning, pytorch, huggingface etc... Knowledge of speech, audio, time-series or signal processing is required. Ability to effectively and efficiently utilise industry-standard Linux-based computers for AI
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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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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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in utilizing machine learning libraries such as PyTorch, TensorFlow, and Scikit-learn. At least 2 years of experience working with Linux computing clusters. Ability to work independently and within a
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related field. Experience The ideal candidate should have demonstrated experience in several of the following areas: Extensive experience with python and modern C++ in a Linux/UNIX environment, including
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proficiency in R and/or Python for statistical modeling and large-scale data analysis; experience developing reproducible computational workflows is preferred Experience with Linux-based environments and large
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cloud platforms for compute and storage. Version Control & CI/CD: Git, automated testing, deployment workflows. Experience with Linux systems, HPC, and distributed computing environments. Knowledge