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Field
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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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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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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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in research and development of sustainable energy conversion technologies. We are recognized as global leaders in this field, supported by state-of-the-art facilities and deep expertise. Our
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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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computational pipelines and deep learning of imaging. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications: No additional
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: Automated tracking of ankle muscle fascicle kinematics in both superficial and deep muscles will allow for the intuitive and coordinated control of powered prostheses following leg amputations. In
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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Title: Postdoctoral Research Associate - Machine Learning & Advanced Manufacturing Employee Classification: Postdoctoral Research Assoc Campus: University of North Texas Division: UNT-Provost