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, computational biology, numerical analysis of partial differential equations (including ice and ocean dynamics), computational statistics and machine learning, as well as algorithm design and discrete
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comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures ( https://CGRlab.github.io/research/ ). About the research project We are open
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as healthcare, cyber-physical systems, and foundation models, including large language models. Depending on the candidate’s interests and background, the project can focus on algorithm design
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learning (ML) methods, and artificial intelligence (AI). This includes working with performance, scalability, resilience regarding platform architectures, and algorithm performance for AI/ML. The
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course work or relevant projects. The ideal candidate is highly motivated with a solid background in one or more of the following areas: algorithm design, machine learning, mathematical optimization
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; comfort moving between algorithm development and hands-on fieldwork; rigour and reliability in data handling; and strong collaboration across academic and industrial partners. Information This is a full