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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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estimated to cause 1.3 million deaths annually. However, drivers of the AMR crisis are still largely unexplored in population cohorts. Also, the amount of sequencing data has increased massively in the last
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, or estimation of future healthcare costs, useful in public health planning. We utilize large datasets from real patient records and registries e.g. from Finland (7 million people) and France (16 million people
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prediction of progression of diseases like cancer, or estimation of future healthcare costs, useful in public health planning. We utilize large datasets from real patient records and registries e.g. from
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no estimate of their correctness which severely hampers accurate estimation of the correctness of downstream analysis. In this project we will develop novel models for estimating the correctness of genome and
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statistical modelling, Solid skills in computer programming for data science (e.g. R, Python), Experience with real-world data analysis tasks, Good communication skills in English, both verbal and written
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, current tools for assembly and compaction of sequencing data produce sequences with no estimate of their correctness which severely hampers accurate estimation of the correctness of downstream analysis. In
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of the biggest threats to human health and is estimated to cause 1.3 million deaths annually. However, drivers of the AMR crisis are still largely unexplored in population cohorts. Also, the amount of sequencing
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Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4 emissions due to the large temporal and spatial variations in CH4