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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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utilize frameworks like Django to support the automation of synthesis, fabrication, and characterization processes. Engage in the development of modular AI-driven platforms for data management, ensuring
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to international conferences Willingness to participate in courses and tutorials Good command of English, knowledge of German is not a prerequisite (language courses are offered) Structured and systematic working
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to international conferences Willingness to participate in courses and tutorials Good command of English, knowledge of German is not a prerequisite (language courses are offered) Structured and systematic working
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considering the potential to apply these methods to different domains. Specifically, you will: Develop, implement and refine Machine Learning (ML) techniques for self-supervised Deep Learning (DL
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the field of energy engineering, optimization, climate change, and climate change adaptation. Software development, particularly in Python will constitute a significant portion of your work. Upon completion
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membranes. You will develop theoretical models and employ computational methods to describe the coupling between ionic nanofluidics, electric double-layer charging, and interfacial reactivity in electrolyte
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preparation Deriving insights for film and device fabrication based on the above described workflow Your Profile: Masters degree in physics, materials science, physical chemistry, electrical engineering and
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their effects on soil hydraulic properties and on root water and nutrient uptake Use the model to quantify the carbon investment into AMF and the related water and nutrient uptake efficiencies Present and
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(Masters) in Electrical Engineering, Mechanical Engineering, Electronic Engineering, Physics or a comparable field Strong mathematical background Excellent knowledge and experience in programming (e.g