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Field
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Ph.D. in any aspects of climate and/or sustainability, with either by a PhD degree in any aspect of climate and/or sustainability sciences and engineering (e.g., to coincide with one of the five themes
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2027 for 19,5 hours per week. The research associate will be working in project “AGEAI: Ageism in AI: new forms of age discrimination and exclusion in the era of algorithms and artificial intelligence
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Responsibilities: Research and develop novel ML-based methodologies and algorithms in LLM-empowered Sub-Graph Learning for Large Graph Models. Working closely with other Postdoc/RA/PhD students to discuss the ideas
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awarded to students who are enrolled in non-award courses, or up to four years, in the cases of students enrolled in a PhD. Scientific advisor: João Tiago Paulo Workplace: INESC TEC, Braga, Portugal
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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successful candidate will primarily work on the development and enhancement of the OASIS model, contributing to new physical parameterisations, numerical algorithms, and/or performance optimisation. They will
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the prediction of molecular crystal structures, their growth and their properties in close collaboration with fellow team members. Developing algorithms for efficient sampling of candidate crystal structures and
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leverage their expertise to develop innovative algorithms for data analysis. Additionally, they will be responsible for communicating their findings to the scientific community through academic meetings and
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dynamical systems. Designing learning-based event-triggered optimal control algorithms to achieve prescribed-time optimal output regulation for uncertain multi-agent systems. Investigating learning-based
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of quantum field theory, mathematical aspects of string theory, general mathematical physics or quantum algorithms and quantum software development. We stress that candidates with an excellent research track