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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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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 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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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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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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Engineering or Chemistry, Physics or Informatics Experience with experimental work and characterization techniques Demonstrated experience in programming, particularly in Python, with a robust understanding of