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. Work with large language models (LLM) and deep learning algorithms to drive the inverse design of materials and uncover new physical and chemical phenomena. Ensure the integration and effectiveness of AI
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interpretable machine learning approaches for the domain of materials science, physics, microscopy Incorporation of machine learning results with simulation methods Working on project-related tasks where you can
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analysis of collected air samples with the GC-HID-ECD system Investigate the climate impact of the measured H2 profiles in collaboration with our modeling department Your Profile: MSc in meteorology, physics
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/electronic engineering, computer science, computer engineering, physics, and related fields. For IC projects a strong electronics background, with experience in design and simulation of analog, digital
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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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, or a related field of study Knowledge of energy process engineering is an advantage Huge interest in energy technology and economics Experience in energy system modeling is beneficial First programming