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in machine learning and artificial intelligence Experience with numerical analysis and scientific computing Knowledge of power systems and renewable energy technologies Experience in power system
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background in Computer Science, Informatics Engineering, Mathematical Modeling, Computational Urban Science, Transport Modeling or equivalent, or a similar degree with an academic level equivalent to a two
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Key Responsibilities (in close collaboration with TU Munich): Develop a deep understanding of materials engineering principles to guide the design and functionalization of nanoporous materials
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. You should have a strong academic background in engineering, applied mathematics, or computer science, combined with a clear interest in scientific programming, machine learning, and data analytics
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engineering challenge. Key to success will be the development of cost competitive and reliable methods to produce hydrogen via electrolysis of water/steam driven by green electricity. Hydrogen can be used as a
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. The overarching goal of this newly funded project is to realize quantum light sources coupled to quantum memories. Quantum memories are key components of optical quantum computers and scalable quantum networks
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of Civil and Mechanical Engineering, Thermal Energy Section. We look for a talented, self-motivated, and team-oriented individual who thrives in a collaborative environment and enjoys tackling complex topics
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of technology, contribute to maritime security, or lead innovations in the growing field of autonomous systems, this PhD position will equip you with the skills, knowledge, and experience to succeed. If you are