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surface composition. The goal is to detect and understand changes in the ionomer and catalyst particles caused by degradation, and ultimately reduce degradation mechanisms in the future. This could increase
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. Additionally, enjoyment of teamwork is an important requirement. Masters degree in electrical/electronic engineering, computer science, computer engineering, physics, and related fields. For IC projects a strong
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activities, semantic and infrastructural engineering in the field of Material Science, e.g. within NFDI-MatWerk Dissemination of developed tools and concepts within interest groups, workshops and training
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and Stuttgart University. Your Profile: You are an enthusiastic and motivated researcher You have a Masters degree and a PhD in physics, chemistry, engineering or related fields Ideally you also have
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Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good knowledge and proven skills with larger deep learning
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the modelling pipeline Your Profile: A university degree (Master) with subsequent PhD in Applied Mathematics, Mathematical Biology, Computational Physics/Engineering or related fields with strong relation
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science and engineering or a related subject Strong capabilities in software engineering and programming are essential. You should have very good knowledge of and practical experience with machine learning
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presentation of simulation results Your Profile: Currently enrolled in a master`s program in process engineering, chemical engineering, or a related field Knowledge in hydrogen and energy research is
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Profile: Masters degree in physics, materials science, physical chemistry, electrical engineering and related disciplines Very good grades in the masters and bachelors degrees are mandatory Knowledge
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and courses Your Profile: Excellent Master`s degree in computer science, science, engineering or in a similiar field Strong software engineering and programming skills Broad interest in scientific