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Venturelli Lab The Venturelli Lab at Duke University (www.venturellilab.org) is seeking highly motivated researchers or postdoctoral researchers with expertise in machine learning, deep learning, and/or
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Alabama and collect pilot data to support larger subsequent research proposals. A carefully assembled team of mentors with deep experience in mentoring and collective expertise across all of the domains
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Department of Energy and Environmental Protection (CT DEEP), related to air emissions, regulated building materials, water and land use, and water discharges and provide guidance to project teams, facilities
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Data understanding Software development of automatic writer/hand identification And will allow you to develop competences in Software management (e.g., Git use) Data preparation to train Deep Learning
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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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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Large Language Model, autonomous driving, deep learning, interaction modelling, prediction, robotics and automation. Candidates having
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advanced analytical and statistical techniques to extract actionable insights from complex datasets. Train, evaluate, and continuously refine deep learning and machine learning models, prioritising
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electromechanical systems hardware and computer vision software to analyze and measure manufacturing processes and systems. Duties and Responsibilities : Design software architecture based on deep machine learning
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or edge AI deployment. Strong programming skills in C, C++, and Python, with experience in deep learning frameworks such as PyTorch or TensorFlow. Familiarity with deployment constraints on cloud, edge
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) for the Romanian language ● programming skills in Python; knowledge of deep learning, machine learning, scikit-learn, Hugging Face, transformers, and LLMs Specific Requirements ● Collecting human-subject data using