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Field
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analysis problems, especially tracking the motion of objects, which are driven by real applications in life science research Developing solutions to integrate large foundation models into microscopy image
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the diversity of aspartic proteases from the model plant Arabidopsis thaliana and deploy chemical synthesis, advanced modelling, protease biochemistry, mass spectrometry and structural analysis methods. A
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these determinants, we will harness the diversity of aspartic proteases from the model plant Arabidopsis thaliana and deploy chemical synthesis, advanced modelling, protease biochemistry, mass spectrometry and
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interdisciplinarity and transfer of science to society. As a modern employer, it offers attractive working conditions to all employees in teaching, research, technology and administration. The goal is to promote and
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management platform that connects institutes to facilitate a rapid and efficient exchange among experimental and computational groups Devising an approach in invertible predictive modeling that links
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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isotope ratio mass spectrometry (IRMS) and gas chromatography coupled with mass spectroscopy (GC-MS), as well as other spectrometric methods Experimental and modelling analysis of relevant processes in
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plant genetic mechanisms that coordinate mycorrhizal interactions with plant P and water status, root system development, and soil microbial communities. Using maize and rice as models, we will: 1
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biological catalysts — under operational conditions. Your Responsibilities: Develop and implement in situ EPR methodologies on state-of-the-art spectrometers Conduct research at the intersection
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Your Profile: An excellent master’s degree with strong background in nano science, materials science, chemistry, chemical engineering, physics or related Experience with modeling of reaction kinetics