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Bayesian statistics, stochastic modeling, and optimization under uncertainty • Proficiency in Python programming • Strong interest in applied and transferable research • Knowledge of industrial simulation
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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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, organization of scientific workshops, and attendance at conferences. Key qualifications include a PhD in a relevant field, expertise in AI/ML (e.g., PyTorch, TensorFlow, Python), interest in materials
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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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basic scripting in Image J, Matlab and Python - Excellent attention to detail and record keeping - Excellent time and project management skills Before applying, please note that to work at McGill
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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
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, or engineering Experience in optical microscopy Independent working skills; Strong communication skills in both spoken and written English to work on an interdisciplinary project; Programming skills in Python
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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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Eligibility criteria Strong skills in analyzing single-cell transcriptomic data using Python. Strong skills in analyzing epithelial tissue dynamics. Website for additional job details https://emploi.cnrs.fr
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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