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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
, western blotting, and cell culture Experience with murine models of bacterial infection Proficiency in experimental design and documentation, data analysis and visualization, and troubleshooting Experience
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naturally infected animals. Most of the effort will be spent on cell cultures, molecular tests, animal studies, and associated data collection and analysis. Start date is negotiable; The successful candidate
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study activities; lead and manage projects. Oversee organization, synthesis and analysis of data and findings. Prepare scientific reports, outcome findings and scientific manuscripts. Participate in
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models Experience in artificial intelligence to develop the algorithms for digital twins Experience with both qualitative and quantitative data analysis techniques Experience in cleaning and analyzing
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workflows of bioimaging analysis to quantify advanced imaging data generated by super-resolution STED microscopy, FLIM, FRET and FCS. Candidates must hold a PhD in cell biology, biophysics or biochemistry
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successful candidate will have previous experience in computer science or data science, with a PhD and publications in at least one of the following areas: Formal modelling and verification of business
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mental health Knowledge of qualitative, participatory, and creative research methods Experience in analysing qualitative data using thematic analysis and analysis software (e.g., NVivo) Demonstrated
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for Research Staff Development for more information. About You To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD in cardiovascular
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for the development of the autonomous quantum processing unit (AQPU) as follows: Carry out design, experiments, and/or data analysis on specific research topics that we consider most important for realizing the AQPU
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and analysis of mathematical methods for novel imaging techniques and foundations of machine learning. Within the project COMFORT (funded by BMFTR) we aim to develop new algorithms for the training