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Field
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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related field Demonstrated experience in interdisciplinary research and excellent digital literacy Strong interest in historical data, machine learning, data visualization, or digital hermeneutics Strong
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for interacting with colleagues and stakeholders. Department Specifics Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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in machine learning, AI and programming skills, e.g. Python basic knowledge of materials science / materials engineering Leibniz-IWT is a certified family-friendly research institute and actively
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-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes) are particularly encouraged to apply. Position 3 Working with Dr. Kelly Baker , EEH Associate
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 3 months ago
holography. We are seeking a highly motivated postdoctor-al researcher to join our multidisciplinary team at the intersection of optics, electronics, machine learning, and atmospheric science. The successful
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deployment. Experience with reinforcement learning (RL), computer vision, and sim-to-real transfer. Experience with robotic hardware platforms such as mobile robots, robotic arms, and embedded sensors
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. The concept has lately gained increasing interest from researchers in applied mathematics and machine learning. This is due to its remarkable flexibility, mathematical elegance, and as it has produced state
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Sustainability in association with Professor Christina Lioma and her Machine Learning research team in the Department of Computer Science at the University of Copenhagen. The sub-package focuses
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difficult and the creation of more intelligent process control strategies and innovative methods of tracking reliability can be achieved with expert informed machine learning techniques, which offer more