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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical technology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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Machine Learning. You can find out more about the potential content that these might include here . There will also be opportunities to contribute to the development of associated new MSc programmes in
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet of Things and Remote Sensing datasets is