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WEKA, VAST, GPFS, BGFS, CEPH. Experience with installing and supporting: Open source and commercial research related software, Python, R, Matlab, Mathworks, Julia, Ansys, Intel, nVidia CUDA and GCC
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datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep learning architectures including generative models, particularly
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paper cutters (Cricut Maker), K-12 robotics kits (LEGO, VEX, littleBits), basic block coding (Scratch, MakeCode), Python, Arduino, micro:bit, Makey-Makey, engineering design projects, etc. Experience
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maintaining network infrastructure, servers, and security protocols. Strong scripting and automation skills (e.g., Bash, Python), including experience with deployment tools, operating system automation, and
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, VBS, and Bash to automate manual administration tasks. Python and Ruby. Experience with version control systems such as git, RCS, and subversion. Ability to comfortably discuss computing topics with
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. Strong programming and analytical skills (e.g., Python, R, GIS, modeling frameworks). Experience working with EO data, disease surveillance data, or socio-environmental modeling. Demonstrated interest in
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Computer Science or related discipline. Experience translating models to software for use in platforms. Equipment Utilized Ability to use PyCharm/Python and other software to produce machine learning. Physical
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Proficiency in scientific computation in C/C++ and Matlab/Python or similar languages Good communication and writing skills The College of Science seeks a diverse and inclusive workforce and is committed
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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thinking skills. Competence in data analysis and visualization using software such as GraphPad Prism, R, or Python. Excellent scientific writing and communication skills, with a track record of peer-reviewed