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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 4 days ago
reconstituted form or directly inside the cell. This approach requires biochemistry or cell biology for the sample preparation and scripting skills for the ET data processing. For more information, please read
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preference in the hiring process. The University of Tübingen is committed to equal opportunities and diversity. It therefore takes individual situations into account and asks for relevant information
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associated PhD topics is highly encouraged Your profile Completed university studies (Master/Diploma) in STEM fields with strong background on process modelling, applied mathematics, process engineering
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of biogeochemical processes with an emphasis on terrestrial ecosystems Development of observational techniques to monitor and assess biogeochemical feedbacks in the Earth system Theory and model development
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. The department of Atmospheric Chemistry (ACD) and Atmospheric Microphysics (AMP) research the chemical and physical properties of aerosol particles and their interactions with clouds. Process-based laboratory
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if they are equally suit-able and qualified. Applications from people with a migration background are welcome. Please note our information on data protection in the application process at https://www.hmtm-hannover.de
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://www.sustainable-pv.de The aim of the project is the integration and selective detachment of ultra-thin layers in solar cells for better recycling, as well as the development of controllable processes
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: Sucessfully completed scientific university degree (Master) in the fields of chemical engineering, process engineering, chemistry or a comparable discipline Knowledge of hydrogen and energy research is an
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teaching and therefore urges qualified women academics to apply for these positions. Equally qualified applicants with disabilities will be given preference in the hiring process. The University of Tübingen
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements