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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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learning, computing, data science, biomedical engineering, or a related field at the level of a master degree Programming skills (Python) and experience with common machine learning platforms Experience with
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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with quantitative methods and data management have experience with programming, for example in Python, R, or similar have an interest in AI, generative AI, learning technology, or business analytics
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are expected to have experience with data analysis in a clinical context, as well as programming skills in R or Python and familiarity with command-line tools. Experience with data management and the
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data analysis; proficiency in Python or a comparable language is expected. A publication record appropriate to career stage that demonstrates the ability to conduct and communicate independent
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experimental data. • Proficiency in scientific programming and data analysis tools (e.g., Python, R, Linux/Unix environments). • Demonstrated track record of publishing scientific results in peer-reviewed
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research questions, and be able to think critically and develop your own scientific ideas. Previous experience with statistical analysis, programming (e.g., R or Python), machine learning, or genomic data
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environments. Experience with software such as R, Python, SPSS, Stata, Sawtooth, Qualtrics or similar tools will be considered an advantage. The successful candidate should have strong analytical skills, good
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wireless communications and/or wireless IoT systems. Strong programming skills, preferably in Python, MATLAB, C/C++ Experience with machine learning and the ability to develop, train, and evaluate data