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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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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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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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, 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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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
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negotiable, preferably in autumn 2025 or in 2026. Background Northern wetlands emit large amounts of methane (CH4), a potent greenhouse gas. There are high uncertainties in the estimation of wetland CH4
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investigation and will develop an advanced computer modeling framework. By simulating processes at various scales, from the atomistic to continuum, we aim to reveal how temperature and saturation fluctuations
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for independent creative thinking Skills in computer programming and experience with Linux and possibly machine learning The appointee should either already have the right to pursue a doctoral degree at the
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Current climate change scenarios estimate the growth zones to shift approximately 80 km northwards in a decade, and the speed is likely to accelerate towards the end of the century. On the other