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(JLU) and the Leibniz Centre for Agricultural Landscape Research (ZALF) e. V. in Müncheberg, invite applications for a W3 Professorship in Landscape Data Analytics according to the Jülich model
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(JLU) and the Leibniz Centre for Agricultural Landscape Research (ZALF) e. V. in Müncheberg, invite applications for a W3 Professorship in Landscape Data Analytics according to the Jülich model
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of the next-generation image analysis and control framework for microfluidic live-cell analytics in close collaboration with partners at HZI, Helmholtz Munich and HHU. Your tasks in detail: Establish deep
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mass spectrometry analyses, including experimental design, data acquisition, and data interpretation. This role involves developing and optimizing methodologies, assisting users from diverse research
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on analytical inorganic chemistry. - Knowledge of decomposition/digestion procedures for complex materials such as rocks. - Knowledge of element separation techniques from complex solutions, e.g. using
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completed bachelor's AND master’s degree in chemistry with a focus on analytical or environmental chemistry from a university or comparable institution of higher education is required. Applicants must have a
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Association. Our institute integrates cutting-edge biomolecular research, bioinformatics, and high-performance analytical technologies to explore the complex interactions between the human organism and food
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into microscopy image analysis and analytical data analysis workflows, together with other team members Build new foundation models for microscopy image analysis Implement AI-based microscopy image analysis
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 7 days ago
30 Jan 2026 Job Information Organisation/Company Leibniz-Institute for Food Systems Biology at the Technical University of Munich Research Field Chemistry Chemistry » Biochemistry Researcher Profile
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into microscopy image analysis and analytical data analysis workflows, together with other team members Building new foundation models for microscopy image analysis Implementing AI-based microscopy image analysis