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project funded within the DFG Priority Programme “Illuminating Gene Functions in the Human Gut Microbiome” (SPP 2474) and be involved in microbiology and molecular microbiology of the gut microbiota
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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architectures, capable of capturing the structure of complex, high-resolution NMR spectra – analogous to how language models such as ChatGPT learn the structure of human language. One of the primary goals is to
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paid PhD position in the area of Natural Language Processing starting as soon as possible. Your responsibilities Research & development projects in the area of NLU and NLG Contribution to teaching on
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of agricultural applications, technology and research. Compensation The position will be paid in accordance with the Collective Agreement for the Civil Service of the German States (TV-L). Your qualifications • A
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collaboration Relevant experience with programming languages (preferably Python) You work independently, in a structured and reliable manner. You are communicative, flexible and able to work under pressure
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forest edges on biophysical conditions and regeneration in the forest interior” funded by the German Research Foundation (DFG). About the position The Professorship of Earth Observation for Ecosystem
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, Machine Learning, Hyperspectral Cameras • Professional proficiency in written and spoken English Application process Send your application in English by email to amx@wzw.tum.de with the title “Research
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for challenging mathematical problems and interdisciplinary collaboration Relevant experience with programming languages (preferably Python) You work independently, in a structured and reliable manner. You are
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, particularly in scientific writing and communication, is required; A good working knowledge of German is preferred to facilitate communication with technical staff and administrative services Basic knowledge