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. Essential qualifications PhD in computational statistics, bioinformatics, systems biology, or a related field. Strong expertise in statistical methods, including machine learning approaches, for analysis
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and qualifications A PhD degree in computational biology, machine learning, computer science, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form
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, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form of high-quality scientific publications, experience working with real-world health data is a plus
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research in computer science at the University of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete
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record Great quality interaction and communication skills. Additional merits .Experience with one or more of the following is considered as an advantage: single-cell omics, bioinformatics, bioimaging, flow
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application domains: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum‑classical algorithms to tackle domain‑specific, computationally
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computing and quantum advantage is a prospective topic across four application domains: bioinformatics, breeding, biomaterial synthesis, and cellulose‑processing enzyme design. You will develop hybrid quantum