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person will focus on either using and/or developing Vlasiator. Prior knowledge in at least one of the following areas is required: GPU technologies, high-performance computing, parallelisation algorithms
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to migration-related diversity. The position contributes to advancing methodological innovation through the creative and reliable use of machine learning, AI, and other algorithmic techniques in qualitative
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their repertoire. If they encounter text written in a language they have not seen before, they label it with what their algorithm deems the closest match. The results of such behavior can vary from the indicated
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, AI, and other algorithmic techniques in qualitative social scientific research. In line with the interdisciplinary and reflexive ethos of DIVSOL, attention to the societal implications of AI is an
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they encounter with a label of one of the languages in their repertoire. If they encounter text written in a language they have not seen before, they label it with what their algorithm deems the closest match. The
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diversity (e.g., composition,size- and spatial distribution of trees) and diversity of other species in the forest are linked. The focus will be on understanding the spatial scales of these relationships and
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project is to develop a high-performance computing framework for mass spectrometry proteomics to enhance efficient processing and interpretation of large datasets using deep learning algorithms and GPU
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analyses of quantitative forest structure and species monitoring data to understand how forest structural diversity (e.g., composition,size- and spatial distribution of trees) and diversity of other species
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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embodied and justice-oriented approaches to datafication literacy, to support human agency and mitigate algorithmic harms. The focus will be on empirical research that uses different design and game-making