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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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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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the standardization and harmonization of data across platforms. Work with large language models (LLM) and deep learning algorithms to drive the inverse design of materials and uncover new physical and chemical
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the Institute of Physical Chemistry in Warsaw 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
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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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H2 profiles in collaboration with our modeling department Your Profile: MSc in meteorology, physics, chemistry, environmental sciences or a related field with good final grade (German system equivalent
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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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of soft- and hardware infrastructure Close collaboration with automation experts, data and material scientists to improve and use a fast and reliable electrochemical testing process Your Profile: Master