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deepest branches of the tree of life with the help of bioinformatics and experimental molecular techniques. Research about how chemicals of anthropogenic origin interact with cellular functions and cause
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efficiently interact in the interdisciplinary project. We seek candidates with a strong computer science, mathematics, statistics, or bioinformatics background and strong programming skills. Some previous
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at least 1 million DNA barcodes. The project involves collaboration with a computer vision lab at Linköping University, focused on developing AI-assisted techniques for picking out specimens for genome
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to the advancement of precision medicine in oncology. A typical workday may involve writing and running code to pre-process sequencing data on a compute server, applying statistical models and algorithms to construct
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) programme and research school Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures
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, as well as handling bioinformatics tasks such such as running analysis pipelines, performing quality control, and conducting downstream data analyses. You are expected to actively contribute to method
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factor – DNA binding, detecting protein – protein interactions or enzyme optimization. Main responsibilities The candidate will use and develop methods within one, or preferably multiple, of the following
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substantially equivalent knowledge in some other way. For this position, the applicant must hold a master’s degree in molecular biotechnology, bioinformatics, computer science, or another area that the employer
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components of organisms’ general life-history decisions. The work involves accumulation of mutations in different fly lines, extracting DNA and building libraries for sequencing, as well as bioinformatics
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service or service/assignment relevant to the subject area. Assesment Criteria Requirements: PhD in bioinformatics, biostatistics, computational biology, data science, machine learning, molecular biology