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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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terms of publications Proficiency in scientific computation in C/C++ and Matlab/Python or similar languages Good communication and writing skills The College of Science seeks a diverse and inclusive
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user friendly scripts in R, and Python for data visualization and analysis that can be used by other team members. ● Creative problem solver with critical thinking skills and the ability to manage
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user friendly scripts in R, and Python for data visualization and analysis that can be used by other team members. ● Creative problem solver with critical thinking skills and the ability to manage
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methods (FEM/FVM) and scientific computing Proficiency in Julia, C++, or Python and experience with HPC environments Interest in geophysical applications, fracture modeling, or multiphysics simulations
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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• Proficiency in at least one statistical software (e.g., R, Stata, SPSS, Python) • Expertise in quantitative analysis, with preferred skills in o Quasi-experimental evaluation techniques (e.g., Difference-in
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communication skills and fluency in English are required. Ideal candidate will be adept at planning, prioritizing, multi-tasking, organizing, following through, and thinking on his/her feet. Knowledge of Python
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-reviewed publications in high-quality journals. • Proficiency in programming and modelling tools (e.g., R, Python, C/C++, Julia). • Excellent quantitative and analytical skills, with experience handling
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framework such as Tensorflow, Pytorch, MXNet and Programming Languages such as Python, Matlab, R and/or C/C++. Demonstrated project experience related to causal inference will be an advantage. Good written