672 machine-learning-"https:" "https:" "https:" "https:" "https:" "The University of Edinburgh" uni jobs in Germany
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the collected data in accordance with FAIR standards for collaborative use within the Decode project and for machine learning applications Presentation of results in team meetings and preparation of a
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with our Welcome Days and Welcome Guide: https://go.fzj.de/welcome FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time ), allow you to
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existing technologies, right through to the tested prototype. The Data-based Methods team at Fraunhofer ENAS develops real-world applications using AI, machine learning and computer vision. The main focus is
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, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following tasks: You will work together
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University Hannover at: https://berufungen.uni-hannover.de Information on the collection of personal data according to article 13 GDPR can be found at https://www.uni-hannover.de/en/datenschutzhinweis
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and experience or willingness to learn methods, in particular the morphological, chemical, and microbiological characterization of plant samples Very good knowledge of statistical data analysis and data
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approach familiarity with the German academic system and the country’s policy landscape We appreciate your willingness to acquire German language skills. The University of Hamburg and the GIGA will provide
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W3 Endowed Professorship for “Hemodynamic Modeling in Atherosclerosis- (f/m/d) KSB Foundation W3 end
exclusively in electronic form and in accordance with the “Applicant Guideline Format Template” and the “Applicant Form” of the Medical Faculty Mannheim for W3 professorships (see https://www.umm.uni
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individually, e.g., through training and networking opportunities specifically for doctoral candidates (JuDocS): https://go.fzj.de/JuDocs FAIR REMUNERATION: Depending on your qualifications and assigned
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for three letters of reference to be submitted via https://www.pks.mpg.de/reference . Please submit all materials by April 3rd, 2026. Apply now Prof. Dr. Frank Jülicher Max Planck Institute for the Physics