30 parallel-processing-"International-PhD-Programme-(IPP)-Mainz" PhD positions at Forschungszentrum Jülich in Germany
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on investigating the change in the catalysts surface under relevant process conditions using spectroscopic analysis methods. Your task will include: Application of established and novel methods for the preparation
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(STEM) and TEM in order to advance our understanding of vitrified biological specimens. Develop novel methods for the application of cryo-(S)TEM methods to biological specimens including the operation of
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Your Job: Your research will explore the integration of emerging energy technologies into future energy systems and assess their potential contributions toward a greenhouse gas-neutral Europe. A key objective is to identify under what technical and economic conditions innovative...
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to enhance the understanding of the soil-root system Find links between non-invasive geophysical monitoring and the availability of Nitrate and water in the soil Improve the small-scale process understanding
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skills and experience with numerical modeling and particle-based methods Interest in working closely with experimentalists Excellent written and spoken English skills Experience with parallel programming
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practices. Within this framework you will: extend and use a process-based modeling approach which explicitly represents microorganisms and biomolecule functioning in soil systems. use process-based modeling
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and develops a wide range of topics related to chemical hydrogen storage along the entire process chain. We place a particular emphasis on LOHC technology, addressing issues across different scales. Our
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or reinforcement learning Interest in (or willingness to learn) plant physiology, genomics and process-based modelling; any prior exposure to crop or ecological models is a plus Proven ability to work independently
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with