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processes with relevance for irrigation and drainage Have knowledge in programming in R and Python for statistical analyses and modelling. This includes experience in applying these skills in projects Have
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For being part of the selection process is needed: University degree (Master/ Dipl. (Univ.)) in a natural science, materials science, or engineering discipline good written and spoken English skills Following
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AI, human-AI collaboration systems, computer vision (CV) models, and natural language processing (NLP) models. Candidates with expertise in any of these areas are encouraged to apply, as the position
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travel Excellent command of written and spoken English Nice to have: Knowledge of academic processes and structures Teaching experience in courses for Master’s students What we offer: Work-life balance
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preliminary idea for a PhD project. A reference check will be part of the application procedure. The vacancy is open for internal and external candidates. In case of equal qualifications, internal candidates
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interdisciplinary training in theoretical language models, clinical approaches, AI methods (e.g., NLP), and neuroimaging, with opportunities to participate in network-wide events and international training activities
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respective training course. Taking initiative comes natural to you. You are a team player with excellent communication skills. You are fluent in English, both orally and written. You are motivated to work in a
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, for that reason you need to fullfill the following requirements: Have good understanding in hydrological and hydraulic processes with relevance for irrigation and drainage Have knowledge in programming in R and
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or because of changes in nutrient conditions. However, the exact nature of mechanistic link between enhanced freshwater flux and biological productivity, and the impacts on carbon export and
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 1 month ago
: we are conducting research on physical one-way functions , reproducible physical processes that generate measurable outputs from input parameters, but for which solving the inverse problem is difficult