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assimilation, and machine learning frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well
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Engineering, Biomedical Engineering, Information Systems, Artificial Intelligence or related areas that demonstrate a solid computational base; Candidates enrolled in a non-degree course: Candidates who exceed
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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proceedings as defined in the project scope and agreements. Scientific activities include: 1) Develop Tidal Resource Models Design and implement statistical and machine learning tidal models to predict energy
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of education and related technical/military/paraprofessional experience may be substituted for a bachelor’s degree on a year for year basis. An advanced degree (Masters or PhD) may be substituted
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and thermal-aware design techniques for embedded systems. Contribute to research outputs and support timely completion of project milestones. Job Requirements: Preferably PhD in Computer Engineering
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commercially orientated research projects in computer vision and machine learning. To be successful you will need: A PhD in Computer Science, Engineering or other Machine Learning-related field. Programming
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machine learning methods to the analysis of large-scale astronomical datasets, with a particular emphasis on time-domain astronomy. Research directions will be flexible and shaped according to mutual
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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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