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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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, and analysis using engineering software and tools for component and system development and laboratory prototype testing. Creativity, adaptability, motivation, independence, excellent oral and writing
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& Analysis Perform quality control, alignment, and quantification of bulk and single-nucleus RNA-seq datasets. Conduct differential expression, clustering, trajectory inference, cell type annotation, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
activity), Western blot analyses, stereotaxic injections, in vivo calcium imaging, data science tools, and statistical analyses. In addition, this position entails publishing research findings in peer
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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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production - assist in the initial phase of the production of a 20-minutes multimedia “digital dialogue” tool (short film with the results of the research). He/she/they will work in person directly with the
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for healthcare. A2 Project or subject specific skills; e.g. experience of data collection, the use of AI/ML tools for processing collected data and understanding of hardware technologies to configure systems and
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analyse large, multidimensional 4D STEM datasets. Develop or adapt software tools (e.g. Python, MATLAB) for image reconstruction, phase mapping, and quantitative analysis of ferroelectric domain wall
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://combatdengue.hemsida.eu/ The researcher will be associated with the work package 2 – DEVELOP high-throughput and super-resolution tools for virus research. The role of the researcher is to design and build advanced optical
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of microbiome-derived proteins as seeds for protein aggregation in neurodegenerative diseases. This project aims to achieve: Advanced annotations of microbiome-borne amyloidogenic proteins Mechanistic insights