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application form: https://wiki.tum.de/display/cpsforms/Ph.D.+Application . Fill out all mandatory fields (*) and kindly use “Safe Autonomous Ships” as the “Title of Position”. Please do not include a cover
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to develop a biogeodynamic model, by coupling a trait-based eco-evolutionary model (e.g. AdaScape) into a reduced-complexity river delta model (e.g. DeltaRCM). We will use the model to synthesise information
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particularly encourage members of underrepresented groups to apply. Applications should be sent by e-mail, together with significant documents (cover letter, your CV, a copy/scan of your degrees), indicating
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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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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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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 11 days ago
candidate has excellent quantitative and data analysis skills, a proven ability to work independently, and a collaborative mindset. They will be expected to lead their own research projects, contribute
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, troubleshooting and routine upkeep Develop, optimize and validate sample-preparation and data-analysis workflows for spatial metabolomics, lipidomics and multimodal studies Act as contact for internal and external
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expected to take place online (via Zoom) on October 10th , 2025. Unfortunately, we cannot cover your application and travel costs. For further information please contact: Prof. Dr. Markus Meier, markus.meier
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order to protect your rights, we ask you to provide us with a clearly recognizable reference to the existence of a degree of severe disability in your cover letter or resume. The position is held
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track-record in first-author scientific publications for Postdoc applications Experience with data-driven machine learning methods for modelling (PINN, Sparse Symbolic Regression methods) High willingness