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Field
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central area of expertise. The successful candidate shall demonstrate deep knowledge of LCA methodology and tools, and show strong competencies in methodological development and application across various
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frameworks such as GTSAM, G2O, or similar; computer vision frameworks like OpenCV; and/or deep learning frameworks such as PyTorch and TensorFlow Prior experience with industry or publicly funded research
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Control engineering (experience with nonlinear systems is a plus) Machine learning and deep learning in context of physical systems Programming skills are required, with Python experience preferred. A good
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artificial intelligence techniques: deep learning or swarm intelligence is a plus but is not required. The annual base salary range for this position is $85,000 - $100,000. When extending an offer
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Preferred Qualifications: Experience with one or more of the following: Building novel 3D and super-resolution ultrasound systems. Developing deep learning algorithms for 3D biological data. Designing and
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of Physics. These projects include development of pixelated Liquid Argon Time Projection Chambers (LArTPCs) for future experiments such as the Deep Underground Neutrino Experiment (DUNE), as
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knowledge about recent neural network architectures for machine learning (e.g., CNNs, RNNs, GANs) have considerable experience with a deep learning framework are curious about the cross-field between signal
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
: * Introductory proficiency in Python, R, or another programming language * Prior exposure to machine learning or AI techniques in clinical research * Experience using deep learning frameworks * Familiarity with
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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sufficient theoretical knowledge of deep learning-based methodologies as well as working with real-world data. Informal enquiries may be addressed to Prof Alison Noble (email: alison.noble@eng.ox.ac.uk