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models for multiple chronic diseases in real-world data and cohort studies. To successfully work in this position, experience of data-driven analytical approaches, machine learning and advanced
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/) at the Medical Faculty, University of Helsinki, is seeking a bioinformatician or a computer scientist with strong analytical skills and experience on omics data, and basic understanding of biology to study therapy
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complex chemistry, molecular metal compounds, homogeneous metal catalysis, organic synthesis, mechanochemistry of metals, as well as experience in analytical techniques including NMR, PXRD, HRMS and ICP-MS
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analytical skills and experience on omics data, and basic understanding of biology to study therapy resistance mechanisms and dynamics in ovarian cancer. Our research focuses on understanding and manipulating
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, conservation biology, biology, environmental sciences, or another relevant field Strong quantitative and analytical skills, including experience handling and analyzing ecological or environmental data (in R
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the research area of the project. Demonstrated quantitative and analytical skills, including programming or modelling experience. Ability to work both independently and as part of a multidisciplinary team
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perception at the landscape scale under different scenarios and employ behavioural experiments and Big Data analytics to understand how changes in time perception influence pro-environmental and sustainable
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scenarios and employ behavioural experiments and Big Data analytics to understand how changes in time perception influence pro-environmental and sustainable behaviours. NATURETIME aims to generate actionable
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on this project will be evaluated on the basis of their application documents and an interview. The successful candidate will have excellent analytic and writing skills in English and will be able and motivated
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quantitative field) A strong interest in life course epidemiology and experience in management and analysis of electronic health records and administrative data Solid analytical skills using statistical