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, R’ or Python programming, Co-supervise PhD and undergraduate students. Be willing to be involved in other research activities different than spectroscopy, such as soil phosphorus, agronomy
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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets
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Python, MATLAB, or C++. Experience with machine learning techniques, CAD, computational modeling, 3D printing, motion capture, and/or material testing. Proficiency in programming languages commonly used in
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proficiency in Python and/or R; familiarity with working in a Linux/Unix environment; fluency in English (both written and spoken); excellent communication and teamwork skills in an interdisciplinary
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pipelines in Python and R. Perform experiments in cell culture and animal models to validate the findings. Coordinate the collaboration between the chronobiology lab (led by Dr. Paul Petrus) and the
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models, statistical analysis). Proficiency in programming languages (e.g., R, Python, MATLAB) and familiarity with relevant air quality modeling tools. Experience with large datasets and data processing
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multispectral/hyperspectral data processing. Proficiency in programming languages such as Python or R for data analysis and processing. Excellent communication skills and the ability to work effectively in a
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the use of bioinformatics tools for metagenomic and transcriptomic data analysis (e.g., QIIME, DADA2, R, Python). Demonstrated ability to independently design and conduct experiments, analyze data, and
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quantitative biomedical sciences. Experience with R and/or Python packages Experience working with Linux, including shell scripting Preferred Qualifications: PhD or MD/PhD degree in Bioinformatics, AI machine