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-management and conservation practices": PhD student (f,m,div) in the Field of Geodata, Nitrogen and Soil Parameter Modelling Reference number: 18/2025/4 The salary will be based on qualification and research
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chemistry and life cycle thinking in early decision-making. AI-based methods and hybrid models, which integrate data-driven and mechanistic modelling, hold significant potential to enhance prospective
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to forward a copy of your CV along with your academic referees. Informal enquiries to:Colin.clarke@nibrt.ie or niall.barron@nibrt.ie Funding:Annual stipend of €25,000 plus fees for 4 years.Project 2: Model
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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conferences Requirements: a university degree in the field of computer science, data science, computational modeling or related subjects in combination with civil engineering, transport engineering a strong
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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PhD position within the project "What's wrong? Ancient corrections in Greek papyri from Egypt" (AnCo
Universities). Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3%), training and career development. Our individual choices model gives you some
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for specific questions. For more information about the School of Computing, please click here To apply, please complete an online application and upload a plain text copy of your CV (2 pages) and covering letter
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to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a distribution of normal cardiac anatomy and function (including motion) from healthy subjects. By