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data from public or commercial databases and develop algorithms using existing libraries. Based on the previously identified resources, the Ph. D. thesis will then focus on the extraction of oxides and
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-source project used by the scattering community Exposure to advanced data analysis methods and practical algorithm implementation Opportunity to work closely with scientists and software developers at MAX
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nuclear and particle physics research leveraging machine learning and AI for data analysis and detector development, as well as exploratory work in quantum algorithms, depending on background and interests
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources
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journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation
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by integrating large-scale single-cell foundation models with structured biological knowledge encoded in genomic graphs. The project will also deliver efficient algorithms to train these models under
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and Correction of Industrial Design Flaws – Towards Fail-Safe Industries) is a Business Finland funded research project with the objective to develop a new type of computer-aided design platform
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on developing the next generation of approaches for analyzing spatiotemporal data arising from sports matches (e.g., tactcal analyses, player/team evaluation, …). See https://dtai.cs.kuleuven.be/sports 4) 1-2
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& AI hardware or brain-inspired AI algorithm development, spatial analysis of multi-omics data. We are particularly interested in applicants with a demonstrated track record of translating discoveries