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Master’s degree in physics, mathematical biology, computational biology or a related subject. They should have good skills in programming languages such as Python, MATLAB or R, and experience in image
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benchmarking tools About you: You have prior knowledge of programming in python and are familiar with machine learning (ML) libraries in python. You have a strong interest in machine learning (ML) and deep
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that provides a sufficient degree of background in computer science, artificial intelligence, mathematics and/or data science. Fluency in English and Python are required. Experience working with real-world
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institutions, to build a framework for discovering, sharing and executing data and algorithms in a distributed environment. The main technology will be Python, though Scala and Typescript knowledge are a plus
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familiarity with Python, social network analysis using tools such as R), and be able to connect with social science questions and methods. The ideal candidate will conduct research at the intersection
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support a project that is evaluating end-of-life options for wind turbine blade composites. Specifically, it applies computer modeling with the CELAVI Python package to quantify and compare recycling
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science Python packages including numpy, pandas, and scikit-learn. Proficiency using relevant tools (Python, R, MATLAB, SQL Server, MySQL) Willingness to learn and quickly adjust to new tools and systems
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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
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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research capability, and experience working within collaborative research environments are essential. Proficiency in Python and a solid grasp of planetary geological processes, especially those relevant