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student projects and BSc/MSc theses Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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and develops a wide range of topics related to chemical hydrogen storage along the entire process chain. We place a particular emphasis on LOHC technology, addressing issues across different scales. Our
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technology Qualified support through your scientific colleagues The chance to independently prepare and work on your tasks The possibility of (location-) flexible working, e.g. home office A large research
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Your Job: You will work in the Electrocatalytic Interface Engineering department, which is headed by Prof. Dr.-Ing. Simon Thiele. The department focuses on the fabrication, analysis and simulation
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Your Profile: An excellent master’s degree with strong background in nano science, materials science, chemistry, chemical engineering, physics or related Experience with modeling of reaction kinetics
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Your Job: The electrocatalytic interface engineering department led by Prof. Dr.-Ing. Simon Thiele focuses on synthesis, manufacturing, analysis and simulation of functional materials to find
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns at the nanometer scale. We will use available