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Science, Machine Learning, Computational Linguistics or a related field, if applicable with PhD previous experience in Natural Language Processing, knowledge Graphs, Machine Learning or Recommender Systems
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opportunity to comment on their assessment. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ . Questions For specific information about the PhD fellowship
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knowledge and practice, as drawn from the discipline of rhetoric and composition/writing studies. Our program’s learning goals are listed here: https://www.umb.edu/academics/cla/english/composition_program
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of Natural Sciences, see: https://fns.uniba.sk/en/study/doctoral-studies/phd-english/admission-and-phd-positions/ PhD research topics in Slovak: https://fns.uniba.sk/studium/informacie-pre-uchadzacov/uchadzaci
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confirmed PhD commencement date: To be confirmed Project Key Words: Machine Translation, Natural Language Processing, Terminology Translation, Agentic RAG Post summary We are looking for a candidate for a
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well as several associated partners (https://www.nnrc.uio.no/english/research-themes/rt-5/ ). The PhD candidate will be part of a dynamic and productive academic team with a large international network covering
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Vector, Vue.js 3, Node.js, Python, and JSON; Natural Language Processing (NLP) applied to collections; implementation of data pipelines using AI models, embeddings (RAG), and OCR; demonstrated knowledge in
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HBIGS Heidelberg Biosciences International Graduate School | Mannheim, Baden W rttemberg | Germany | about 9 hours ago
your application exclusively through the HBIGS application system: https://www.hbigs.uni-heidelberg.de/en/your-application Additional comments Given the interdisciplinary nature of the project, the PhD
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chapter and contribute to other writing projects as required due to the collaborative nature of the project, regular physical presence is expected Where to apply Website https://www.academictransfer.com/en
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and community-based support. This project builds on prior work using Natural Language Processing (NLP) to analyse electronic health records (EHRs) across multiple NHS Trusts. In a dataset of over six