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industrial partners and is partly externally funded by the KK Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods, algorithms, and tools
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within tissues using our in-house developed spatial transcriptomics-based technology (Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both
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the developmental rules underlying phenotypic variation. The successful postdoctoral fellow will develop and implement an empirical framework that utilizes data-driven algorithms to learn relationships between past
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passive and active flow control algorithms, potentially incorporating machine learning/AI, to enhance aerodynamic performance and stall delay with rapid response times. The research is conducted in
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include the design and implementation of finite element multiscale models and machine learning algorithms, analyzing related experimental data, and collaborating with industrial collaborators to validate
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within the Research Area of Inflammation at CMM. Develop new analytical algorithms to address complex, high-dimensional data. Engage in local collaborations at Karolinska Institutet and with external
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set of alternative ways of evaluating a particular expression with unspecified matrix sizes. When a concrete expression is evaluated at run-time, thus revealing the matrix sizes, an extraction algorithm
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layered dependability framework to ensure both safety and security. This involves creating advanced safety control algorithms, system dependency modelling, and fault propagation and traceability methods
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particular expression with unspecified matrix sizes. When a concrete expression is evaluated at run-time, thus revealing the matrix sizes, an extraction algorithm can identify an optimal evaluation scheme