86 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" PhD research jobs
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, MONAI) Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not
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methodologies, including the manipulation and processing of satellite imagery and data from equivalent remote-sensing platforms (e.g., UAVs), as well as the development of machine-learning approaches to enhance
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behavioral well-being, particularly among vulnerable populations. Applicants are encouraged to approach these topics from perspectives such as human-machine communication, mobile communication, and health
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 3 months ago
to gain exposure to and training in state-of-the-art uses of machine learning for solving complex optimization problems with realistic applicability in real-world investment settings. The position is ideal
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, etc.), and data-driven methods (optimisation, generative AI, agent-based modelling, machine learning). Our work provides decision support for policy makers, industry stakeholders, and researchers by
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remuneration, or a proportional part for periods of less than one year. 2.5. Tasks to be carried out: · Developing advanced tools for the spectral analysis of the X-IFU instrument. · Developing machine learning
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autonomous driving. Your profile Master's degree in Computer Science, Artificial Intelligence, Robotics, or related field Strong background in machine learning, deep learning, or computer vision Experience
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, including Python and Git/Github; data analysis experience, including AI and machine learning methods; and a broad knowledge of college sports and/or elite-level competitive athletics. Special Instructions
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error correction (advantage) Background in software development (advantage) Optimization techniques and machine learning (advantage) Ability to conduct independent research Excellent written and oral
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focuses on advanced methodologies in abdominal imaging, particularly applications of machine learning and deep learning to medical image analysis. The lab aims to advance existing imaging techniques and