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aims at developing tools using large language models (LLMs) for the correction of misinformation about climate change in social media. The successful candidate will develop innovative tools leveraging
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model of human working memory. During the project, we will follow an intertwined model-experimental approach. First, we will use electroencephalography (EEG) experiments to investigate the neural basis
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will use computational models to explore the minimal functional requirements for self-replication to emerge from polymerising molecules. Instead of simulating specific chemistries in full detail, we will
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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science, computer science, applied mathematics or a related field a strong background in machine learning, material modeling, and metals processing, modeling and simulation (This will be a clear advantage
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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the Standard Model. The LHCb experiment at CERN is uniquely equipped to study decays of heavy hadrons with high precision. The prospective PhD researcher will take on a leading role in a search for lepton
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are needed for the application process.) - Copies of MA diploma (or equivalent) and list of grades. - Copy of MA thesis (or equivalent). A language certificate is required if the thesis is not written in
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of recommendation are needed for the application process.) Copies of MA diploma (or equivalent) and list of grades. Copy of MA thesis (or equivalent). A language certificate is required if the thesis is not written
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their appointment. Willingness to move and reside in the Netherlands. Do you want to become a member of our team? Please send your application to us, by submitting the following documents in English: Motivation