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Steinrück), we investigate mechanisms and processes of chemical hydrogen storage using advanced methods (including large-scale research facilities) as well as modern data reduction and data evaluation methods
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degree in Physics, Materials Science, Computer Science, Data Science, or related fields Proven experience with large language models (LLMs), natural language processing (NLP), and fine-tuning techniques
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-08187-1 Your Profile: Master and PhD in biology, genomics or bioinformatics Strong background in data science or machine learning (deep learning, statistical modeling, or large-scale data analysis a plus
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) is an advantage Experience in software tools such as Origin, Matlab, Python/Matplotlib or similar programs for data processing and evaluation. Knowledge of a relevant programming language complements
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sequencing, large-scale genomic, transcriptomic, proteomic, metabolomic, and phenotypic data) using cutting-edge technologies, such as machine learning You will perform transcriptomic and epigenetic analysis
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models using experimental data for precise mapping of real processes Conducting detailed analyses of thermomechanical stresses in electrochemical converters using the finite element method (FEM
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at large scale facilities Establishment of cooperation projects with energy-related institutes at Forschungszentrum Jülich Initiating grant applications Supervision of MSc and BSc students Presentation
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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focus on neutron spectroscopy as main analysis technique, supported by complementary experimental techniques or theoretical simulations Hands-on participation in experiments at large scale facilities as
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physics or related with a background in the field of experimental quantum information Willingness to work in laboratory and cleanroom environments Ideally, initial experience in a technical or scientific