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Field
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multidisciplinary experience. Knowledge in applied computer science, particularly in machine learning; in fluid mechanics, especially in hydrodynamics; and in electronics, particularly in instrumentation and
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research paper as first or co-first author You have some experience in experimental work Desirable but not required/ Nice to have A strong foundation in machine learning and statistics You are experienced
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the application of machine learning techniques (e.g., doc2vec, encoder models, multi-modal embeddings, large language models) to map concepts and their relationships, tracing how they change, merge, or diverge
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Responsibilities Conduct and disseminate research on human-computer interaction, with interest and focus on considering learning aspects in design; or in educational technology with interest and focus on interaction
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chains. Has demonstrated experience analyzing textual data using NLP or other machine learning techniques. Has excellent English-language academic communication skills (both written and oral) – CEFR level
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At the Technical Faculty of IT and Design, Department of Sustainability and Planning (PLAN), a Postdoc position in Satellite Data Processing and Machine/Deep Learning is open for appointment from
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institution of TUM Campus Heilbronn that uses data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning
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-coordinating internal scientific events and activities. What we ask of you The successful candidate will have: PhD degree in Human-Computer Interaction, Urban Sciences, Disability Studies, or related
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: 278964401 Position: 2025 Postdoctoral Research Associate - AI/machine learning for analytical and forensic chemistry Description: The Skinnider Lab at Princeton University aims to recruit a postdoctoral
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/english/research/projects/activate/index.html The researcher will be part of a growing team of researchers, postdocs and PhD students working on intelligent observing systems using machine learning and data