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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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-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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, physics, computer science, mathematics, electrical/electronic engineering or a related subject Strong programming skills (Python) Familiarity with machine learning and deep learning frameworks (e.g
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researchers and students Explore new funding opportunities Or, in other words, build up your own research field! Your Profile: Master degree and doctoral degree (PhD) in electrical engineering, physics
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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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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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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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an existing simplified model Your Profile: You are studying engineering, industrial engineering, energy technology, physics or a comparable course of study. You are also characterized by: An interest in
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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