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- Jagiellonian University
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- Institute of Dendrology, Polish Academy of Sciences
- Institute of Plant Genetics PAS
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13 May 2024 Job Information Organisation/Company Institute of Dendrology, Polish Academy of Sciences Department Department of Genetics and Environmental Interactions Research Field Biological
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and molecular methods in the field of neuroscience, will design, conduct and analyse neurophysiological experiments to explore neuronal mechanisms at the cellular and molecular level. In addition
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on posttranslational modifications of engaged proteins and interactions between the proteins involved. Polymerase iota is the most mutagenic human DNA polymerase, but its cellular role remains elusive. The necessity
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fragment and state the starting point for designing new inhibitors interacting directly with CMGC-insert. Requirements Research FieldBiological sciencesEducation LevelMaster Degree or equivalent Skills
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(member of the teaching staff - 360 teaching hours per academic year), artificial intelligence, designing database systems, advanced database systems, software engineering, client-server application
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-creation and co-designing local climate adaptation strategies identification of key components of climate adaptation systems and their interactions (especially the context of extreme phenomena in Gdańsk
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Position type: adjunct or research assistant Research areas: plant biology, microbiomics, plant-environment interaction Type of contract: full-time Salary range - minimum (gross) for: adjunct 6841.00 PLN
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of interaction between the intracellular Wolbachia bacteria and insects. The project will be carried out using molecular biology techniques, next-generation sequencing (NGS) and confocal microscopy. This position
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of Evolutionary Immunology at the Jagiellonian University, Faculty of Biology. The employed person will be responsible for conducting research in the field of host-pathogen interactions on a zebrafish model using
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learning methods for signals, including computer-human interaction techniques developing methods for model evaluation for signals developing models trained in an unsupervised and self-supervised manner