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the complete chain from materials properties to process design and evaluation. More information on the project can be found here: This specific project (DC7) addresses membrane adsorbers, which have small
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-economically cost-optimal design of a process chain for the production of green hydrogen and methanol, it is necessary to carefully analyze both global and regional demand and to estimate their future
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networks using the numerical simulations Calculating the likelihood of material parameters correctly describing experimental results Correlating material parameters with process conditions of sample
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of the frequency-dependent measurements Determination of resistive and non-radiative recombination losses due to the different layers and their interfaces Your Profile: Bachelor Degree in physics, material science
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information from experiment data Evaluation and development of specialized and/or interpretable machine learning approaches for the domain of materials science, physics, microscopy Incorporation of machine
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found using the classical algorithms (from 1) with Quantum Optimization Ansatz (from 3) Your Profile: Enrolled as master student in a university Good background in physics, electrical engineering
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Raman spectroscopy Close collaboration with internal and external cooperation partners Detailed evaluation and processing of measurement data Your Profile: Ongoing master studies in physics, chemistry
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: Very good performance in your Master`s degree in the fields of energy technology, mechanical engineering, materials science, civil engineering, industrial engineering, physics, resource management
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to educational events, such as university lectures, JSC courses and hackathons. Your Profile: Excellent Master in Computer Science, Mathematics, or Physical Science, Very good knowledge and proven skills with
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in physics, physical chemistry, chemical engineering, or related field Knowledge and competences in physical theory and modelling Experience and interest in computer programming Basic knowledge