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The University of Turku

Turku, Finland

The University of Turku (UTU) is Finland’s second largest university. It was established in 1920 but its roots reach back to the Royal Academy of Turku in the 1650s. Today, the University of Turku UTU has almost 20,000 students and 3,271 staff members (8.8% international; 58.6% female). In the international QS ranking, the University of Turku is among the top 300 universities (2019), and a member of the Coimbra Group, a network of prestigious universities in Europe. In June 2013, the European Commission awarded the University of Turku the right to use the HR Excellence in Research logo. This is a token of the University's commitment to continuous development of the position and working conditions of researchers along the guidelines set forth in the European Charter for Researchers.

The Faculty of Science and Engineering was founded in 1920 when the university was first established. Approximately 4,200 are enrolled, with an annual intake of 500 students. The Faculty’s strategic scientific focus lies in biosciences and mathematical methods, and multidisciplinary cooperation is common within the research fields. The research group of Associate Professor Leo Lahti at the Department of Future Technologies was established in 2016, and it is internationally recognized for its applied methodological machine learning research in microbial ecology and bioinformatics. PI Lahti has 8 years of international experience in this research area over the past decade. In addition to publishing research and open software, his team research group has led invited international PhD-level workshops in this area in four countries in 2019 (Belgium, Netherlands, India, Singapore), and coordinates Finland’s participation in the ongoing COST action on statistical and machine learning techniques in microbiome studies.

Group Expertise

Professor Leo Lahti is specialized on data science, and microbial ecology and bioinformatics methods development. Our research focuses on computational analysis and understanding of complex natural and social systems. There is a great demand for targeted computational techniques to extract information and insights from rich data collections based on clever combinations of human and machine intelligence. We blend elements from fields such as machine learning/AI, probabilistic programming, statistical ecology, and data science, and drive open developer communities that help to translate latest theoretical advances into accessible methods to inform modeling, experimentation, and decision-making.

The University of Turku

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952914

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