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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in
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collaborative labs develop and deploy the latest technology, including sensing, data analytics, modelling, simulation, artificial intelligence, and machine learning, and function as dynamic hubs where innovative
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experience and skills: master's degree/engineering degree in artificial intelligence/computer science/applied mathematics; demonstrated abilities in research or problem solving tasks (public challenges, coding
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, Education, Artificial Intelligence or a related field; The skills and knowledge for in-depth automated text analysis and the development and use of text quality metrics; The skills and knowledge for carrying
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completed or near-completed MSc degree in Artificial Intelligence, Computational Linguistics, GeoInformatics, Geography, Geoscience, or a related field; experience with natural language processing (NLP
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(or will soon complete) a Master’s degree in Cognitive Neuroscience, Biomedical Sciences, Artificial Intelligence, Computer Science, Bioengineering, Cognitive or Experimental Psychology, Neurophysiology
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-cognition-relationship and see yourself in the description below: Holds (or will soon complete) a Master’s degree in Cognitive Neuroscience, Biomedical Sciences, Artificial Intelligence, Computer Science
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, Computer Science and Artificial Intelligence. The environment fosters truly interdisciplinary research with a great track record of national and international collaboration transferring knowledge across domains
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Master’s degree in Data Science, Artificial Intelligence, Computational Linguistics, Computer Science. Has excellent academic writing and oral skills in English. Has experience with large language models
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, Computer Science and Artificial Intelligence. The environment fosters truly interdisciplinary research with a great track record of national and international collaboration transferring knowledge across domains