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, collaboratively developing your own research line and publishing in leading machine learning conferences (e.g., NeurIPS, ICLR, ICML) and scientific journals; Actively build bridges with experimental groups within
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Vacancies PhD on Integrating Sustainability and Systems Thinking in Engineering Education Key takeaways The CLEAR initiative (Chemistry Learning for Environmental Action and Responsibility) is an
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to obtain this; You are proactive in developing research collaborations and have the ambition to acquire external funding in interdisciplinary settings; You have excellent communication skills and are fluent
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collaboration with industry partners and SMEs. You have a track record of scientific publications in relevant venues. You are proficient in Python and relevant machine learning frameworks such as PyTorch
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(HIMS), in close collaboration with industrial partner BOR-LYTE and Smart Industry testbeds. This position offers a unique opportunity to combine inorganic chemistry, spectroscopy, machine learning, and
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of training activities and actively contribute ideas to improve our programmes. Your responsibilities will include: supporting the implementation of training and learning activities in close collaboration with
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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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forensic psychiatry collaborate with societal stakeholders, including youth organisations and police, to improve emotion regulation at moments when it is needed most. The project is a unique collaboration
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causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via consultations and collaborative research, train researchers through workshops, and mentor
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and democratic participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal