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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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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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assemblages and morphometrics, sedaDNA and the deep microbiological biosphere), as well as applying other dating techniques including radiocarbon, OSL and palaeomagnetics. In addition to having the opportunity
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using machine learning and deep learning techniques to generate indicators that allow remote monitoring of restoration. Knowledge of remote sensing (e.g. GEDI, LiDAR, multispectral) and programming (e.g
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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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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 the DOE. As a result, fellows will gain deep insight into the federal government's role in the creation and implementation of energy technology policies; apply their scientific, policy, and technical
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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
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on the training strategies. In this project, we will investigate Bayesian methods to train deterministic SNNs (with deterministic activation functions) or probabilistic SNNs. Bayesian deep learning methods have
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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