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qualifications needed (particularly the habilitation), and as a chance to develop a distinct, visible research profile within the academic community. Completed domestic or foreign doctoral/PhD studies in the field
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probing experiments Data analysis Publications in peer-reviewed journals and presentations at international conferences Supervision of trainees and students This is part of your profile: A PhD degree in
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the research team around Univ.-Prof. Dr. Philipp Grohs. Our ideal candidate has a strong interest in Applied Mathematics, possesses solid and profound background knowledge in Numerical Analysis, Approximation
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analysis strategies including phospho/proteomics and metabolmics, selected cell culture and animal model systems as well as clinical samples will be investigated. Your future tasks: Active participation in
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Assistant (Postdoc) to join the research team led by Univ. Prof. Olga Mula. Our group’s work sits at the forefront of numerical analysis for Partial Differential Equations, enriched with data-driven
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on social development in older age. In addition to the content-related fit, sound knowledge in the analysis of longitudinal and experience sampling data is explicitly expected. The tasks include
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carbohydrates from different food matrices. Engaging on the optimization of methodologies for the analysis of novel quality markers, with special focus on mass spectrometry and nuclear magnetic resonance
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of social sciences a successful dissertation with a focus on the above-mentioned research areas proven research competence through publications good IT user skills and knowledge of social science analysis
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knowledge of statistics (R) Experience with multiverse analysis and open science practices Experience in the measurement and evaluation of eye tracking data is desirable Experience with physiological data
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the characterization and analysis of porous materials An excellent background in X-ray diffraction, electron diffraction, electron microscopy and related analytical techniques Open to learn new analytical tools, like