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solutions with real-world impact. Major responsibilities Conduct independent research and collaborate with others in the research group. Supervise Master's and/or PhD students (no teaching included
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tasks include development of adequate single-molecule labeling strategies, optimized use of high-precision MINFLUX microscopy, and establishing methods for data precision analysis. What we offer A
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bioinformatics methods have made significant strides, AI approaches - particularly deep learning - are revealing patterns and relationships in biological data that were previously inaccessible. As a postdoctoral
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sequencing and synthesis to design useful cell behaviors. The scope of this project is to combine multi-gene control technology and computer algorithms to develop a foundational discovery platform for future
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authentication and usable security (e.g., biometrics, CAPTCHA); Privacy (e.g., information inference); Intersection between regulatory and technical aspects (EU NIS-2 Directive), e.g., compliance optimization and
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mathematical modeling of infectious diseases applied to grided spatiotemporal data inputs. The project involves reviewing existing literature and especially synthesize model structures and parameters for the eco
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processing, information and communication theory, machine learning, complex networks, and optimization. The employment This employment is a temporary contract of two years with the possibility of extension up
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systems, diagnostics and prognostics, and modeling and control of vehicle functions. Read more here: https://liu.se/en/organisation/liu/isy/fs . For more information about ISY, click here: https://liu.se/en
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employment may also be extended in the event of absence due to illness. Assessment criteria A basis for the assessment is the applicant’s ability to disseminate information and communicate; collaborate and
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national and European projects that focus on both fundamental and applied research. The Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) addresses data-driven methods to gain