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Field
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assist in research and development efforts related to controllable generative AI for synthetic image generation.Tasks: Deepen understanding of controllable generative AI concepts and methodologies
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and validate them across multiple cancer cohorts Link CIN programs to outcomes and therapy response using large public datasets and modern predictive modeling Integrate CIN signatures with functional
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for research areas are regenerative business models (how to create, deliver and capture multiple forms of value - economic, social, and environmental, human and non-human - in a regenerative context
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focusing on the development and validation of an integrated phenomics–genomics framework to identify novel root architecture and physio-morphological traits conferring pre-anthesis drought tolerance in wheat
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routing decisions to minimize total costs, including transportation costs, inventory holding costs, and shortage costs at various locations (nodes) over multiple time periods. In real-world applications
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on developing a new multi-disorder prediction approach that integrates different sources of information. You work with analytical model development, extensive simulation studies and analysis of existing large
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dataset(s) - Development of new survey and collection of original survey data - Development and implementation of experimental studies - Writing and contributing to scientific papers for peer
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context and their operational constraints. "Systems Design” represents an engaging symbiosis of proven research frameworks to address this challenge, integrating the points-of-views of multiple research
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possibility of extension) Start date: August 2026 at latest The students will be enrolled in the structured PhD programme in Computer Science at Sapienza University of Rome, Italy: https://www.uniroma1.it/en
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(www.inesc-id.pt , “Instituto de Engenharia de Sistemas e Computadores: Investigação e Desenvolvimento em Lisboa” is a Research and Development and Innovation Organization (R&D+i) in the fields