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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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optimization. Practical experience with industrial or collaborative robotic arms (e.g., UR, KUKA, ABB). Proficiency in programming languages such as Python, C++, and ROS. Experience working on interdisciplinary
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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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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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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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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
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, automatic testing, program synthesis Excellent background and experience in programming language, especially python Good communication and report-writing skills Strong responsibility for research and work We
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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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, or a related field. Expertise in deep learning, multimodal LLMs, and Vision-Language-Action models. Strong programming skills in Python, with significant experience in PyTorch. Hands-on experience with
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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