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. Data Protection Information: When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of
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of women in those areas where they are underrepresented and therefore explicitly encourages women to apply. Further information can be obtained from Dr. Béla Majorovits (email: bela@mpp.mpg.de
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Germany Web: https://www.mpinat.mpg.de/grubmueller Social Media: https://bsky.app/profile/compbiophys.bsky.social www.mpinat.mpg.de/ Information pursuant to Article 13 DSGVO on the collection and processing
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recruiting is decentralized - your application will be reviewed directly by the specialist department in which you could work in the future. More information about our application process Please send your
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and in a team. For further information, please contact Mr. Prof. Dr. Lars Heinke (lars.heinke@fu-berlin.de / 030 838 68107). Applications should be sent by e-mail, together with significant documents
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, systems biology, bioengineering, chemical engineering, or a related discipline Knowledge and experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and
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research strategy, including fundamental and applied approaches You will create, process, analyze, and interpret the research data, prepare reports, and scientific publications You will take part and present
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or extensive publications can only be returned if an adequately stamped envelope is attached. If you apply, we collect and process your personal data in accordance with Articles 5 and 6 of the EU GDPR only
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pursues equality for all groups of people. We promote the professional development of women at the institute and strongly encourage them to apply. You can find information on our representative body
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine