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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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processing, and sensor integration. Strong background in signal processing, computer vision, or machine learning. Proficient in programming languages such as Python, C++, and MATLAB. Strong publication record
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systems using computer vision, quantitative image analysis, deep learning methods for detection, diagnosis, and quantitative analysis of abnormalities with multimodal data, including clinical and
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engineering, as well as computer vision, imaging, hyperspectral imaging, machine learning techniques in food and agricultural area, is desired. A understanding of modern AI model development Stipend $70,000.00
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 3 hours ago
innovative research in the fields of data science, data analytics, statistics, machine learning, bioinformatics, or quantitative biology. They should also present a vision for the activities they will
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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Learning (AIML), driving the growth of Industrial AI across key areas such as natural language processing, computer vision, and machine learning. In this role, you will undertake cutting-edge research
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Science and Artificial Intelligence, with a focus on visual reasoning and robotic systems. The Research Fellow position involves developing novel approaches that integrate computer vision, natural
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various cash and cover crop management practices in U.S. agriculture. Experience in machine learning. Strong computational skills. Strong oral and written communication skills. Stipend $63,940.00
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP