29 parallel-processing-bioinformatics positions at UiT The Arctic University of Norway in Norway
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Stig Brøndbo 4th August 2025 Languages English Norsk Bokmål English English Faculty of Engineering Science and Technology PhD Fellow in signal processing and modelling in the seafood industry Apply
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questions concern the role of previously acquired languages and the impact of linguistic distance in this process. An additional relevant issue is how parallel language learning is shaped by the local
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, including metalinguistic awareness and learning processes more generally. Parallel language learning refers to situations where a child (from school age and upwards) or an adult must learn two new languages
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utilizing human cell cultures (2D and organoids), advanced fluorescent imaging, live imaging, FACS, RNAseq + bioinformatic analysis, Click-IT technology, RT-qPCR, Western Blot, and possibly animal experiments
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November 2024) and aims to fill a major research gap in Arctic science by investigating processes, consequences, and impact of past “greenhouse” (warmer than present) conditions. In i2B we will retrieve new
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in Bremerhaven (Germany). i2B is funded for 6 years (starting in November 2024) and aims to fill a major research gap in Arctic science by investigating processes, consequences, and impact of past
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. The research groups are also active in entrepreneurship, clinical process innovation, and industry collaboration. Contact Further information about the position and UiT is available by contacting: Professor
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that demonstrate development over time, as well as a description of and reflection over the process and result. Description of experience with supervision at master's and PhD level. In addition to describing
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remitted. We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act information about the applicant may be included
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the group's research on developing novel machine learning/computer vision methodology. The focus of this project will be on the development of deep learning methodology for spatio-temporal medical image