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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning
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PhD or equivalent degree (e.g., ScD, MD, DVM, DrPH). Under the guidance of Dr. William Pan, the Postdoctoral Fellow will perform full-time, independent research, consistent with preparing for a future
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level, developing and employing machine-learning tools for predicting antibody-epitope binding. In silico antibody design is a long-standing computational and immunological problem. Improving
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applications to external bodies. We’re looking for a highly motivated scientist with: A PhD in Physics, Chemistry, or Biochemistry. Proven expertise in Raman/IR spectroscopy & machine learning classification
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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genetic variation into elite germplasm. This will require the integration and optimization of several technologies, including genomics, machine learning, genetic simulation, and speed breeding. This is a