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Field
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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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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approaches Applying statistical modeling, causal inference, and machine learning approaches to identify determinants of developmental robustness Applying causal inference approaches to identify critical
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modelling, or machine learning). Proficiency in statistical programming (e.g., R, Python, Stan, or similar). Experience developing and managing longitudinal studies. Evidence of contributions towards
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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computational electromagnetics and electromagnetic simulation techniques. Experience in AI-based RF transistor modelling is highly desirable. Solid knowledge of machine learning algorithms and their application
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conditions. The researcher will also work with team members within the consortium in generating necessary data required for developing a machine learning model for storm surge prediction. Key Responsibilities
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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Strong analytical, research, and scientific writing skills Proficiency in Python for modeling and machine learning applications Effective communication, teamwork, and presentation abilities Self-motivated
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assimilation, machine learning, and seasonal weather forecasts. As a Postdoctoral Research Fellow, you will play a crucial role in developing and testing statistical models for the accurate forecasting