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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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development of a newepilepsy research project jointly run by King’s College London and Swansea University and funded by the Medical Research Council. The Research Fellow will be using Natural Language
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of
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, or other related fields. Solid Mathematical skills. Experience in implementing algorithms for machine learning and natural language processing-related applications (It would be good if the candidate can also
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Training Group (RTG) “Activation of anti-Microbial Effectors (ACME)” (coordinator: Dirk Schlüter, Hannover) funded by the German Research Foundation. The central aim of the program is to give highly
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys
UiO/Anders Lien 25th August 2025 Languages English English English PhD Research Fellow in Chemistry-focus on Experiments in Chemical Kinetics and Heterogeneous Catalys Apply for this job See
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from experimental systems biology assays (PMID:36217030 [Nature Biotechnology 2024 & 2023 [Journal Cover]), artificial intelligence (PMID:35411337 [Nature Machine Intelligence 2022]), genomics and
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areas: Time-series analytics or forecasting Natural language processing (especially question answering or language grounding) Multimodal learning (e.g., combining text with temporal or numerical data