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and Data Science (MIDS) at the KU Eichstätt-Ingolstadt. The research group works at the intersection of analysis, modeling and simulation. The advertised position is partly funded by the German Research
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learning, image analysis, and advanced computing to study relationships between structure and function. Keywords: Human Brain, 3D Atlas, Deep Learning, Temporal Lobe, Brain Function Entry Requirements
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conditions chemical analysis of plant secondary metabolites using HPLC-MS and GC-MS protein isolation and characterization using LC-MS plant physiological measurements molecular biological investigations (e.g
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heavily on centralized, time-consuming laboratory analyses and manual adjustments. This is especially problematic for critical indicators such as Chemical Oxygen Demand (COD), a key aggregate parameter that
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welcome) Entgeltgruppe 13 TV-L/75% to be filled. Starting date is 10/1/2025. The position is for three years. The positions are advertised as part of the Research Training Group 2491 "Fourier Analysis and
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as the second most abundant ingredient (Villarreal-Chiu et al., 2017). While these surfactants are considered inert in terms of their effect on glyphosate efficacy (Mesnage et al., 2019), a critical
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ecological challenge and substantiating a critical tool for water resources management, this project offers a great pathway, equipped with tools, data, and mentorship. Research goals: Develop a numerical
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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into goal-directed behavior. We use state-of-the-art approaches including functional brain imaging, automated behavioral analysis, and computational neuroanatomy. We value a collaborative atmosphere, early
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not necessarily conserve mass, as they used black-box model structures far away from that of rainfall-runoff models. For these reasons, neural hydrology is often criticized by the conventional