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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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field, expertise in AI/ML (e.g., PyTorch, TensorFlow, Python), interest in materials applications, and a strong publication record. The position is for two years, with the possibility of renewal, subject
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: Cuantificación de Incertidumbre Bayesiana (Bayesian Uncertainty Quantification, BUQ) Appl Deadline: 2025/10/30 11:59PM * (posted 2025/09/08, listed until 2025/10/30) Position Description: Apply Position
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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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, 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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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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disciplines, including biomathematics, biostatistics, and molecular biology. The candidate is expected to have a solid grounding in programming in R, Python, and mathematics/statistics.The main duties involved
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, Python, Matlab, and C++) would be advantageous. The appointee will work with a research team to study the methodologies for estimating population immunity against infectious diseases, to analyse large and