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Field
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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 Planetary Science, Applied Mathematics, Physics, or a closely related field, completed by the start of the appointment. You should have strong scientific programming skills (Julia, Python, Fortran or C++) and
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blow moulding. You will also have: Experience applying AI or coding tools (e.g. Python or MATLAB) to analyse scientific data A track record of conducting and interpreting experimental work The ability
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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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), including during a postdoctoral fellowship. Experience with high-performance computing centers as well as with modern statistical software (R, etc.) and programming languages (Python, etc.). The candidate is
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molecular dynamics and machine learning. The candidate should have proficiency in scientific programming with Python or C++ and familiarity with machine learning libraries like Torch or TensorFlow
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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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, 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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, 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