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Post doc position in theory of machine learning at Department of Computer Science, Aarhus University
is on understanding and improving the performance of classic learning algorithms, in particular Boosting and Bagging, both in terms of speed and generalization capabilities. The project also allows
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-based topology optimisation and de-homogenisation Adaptive meshing algorithms for topology optimization PDE-driven topology optimisation methods Research fund application Collaboration with industrial
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for human control of complex robotic systems with high levels of agency and minimal cognitive effort. Short description: This project will develop novel AI algorithms to decode human intention from
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integrate machine learning algorithms and Earth System Models to emulate carbon processes in the ocean connected to the biological activities. You will be enrolled in DTU’s Section for Oceans and Arctic and
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separate job advert “Postdoc in advanced reconstruction algorithms for time-resolved nano CT of sustainable cement ”) Research and teaching efforts at a section and departmental level as appropriate and
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algorithm development in the biomedical context. We are particularly, but not exclusively, interested in candidates with competence in multi-modal data integration, including electronic health records, omics
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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Job Description Postdoc position in Innovation and Artificial Intelligence The Algorithms, Data and Democracy project (ADD) and Center for Organizational Datafication and its Ethics in Society
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biased, outdated, or sensitive data? That's where the project TRAI comes in. This research project aims to develop machine unlearning algorithms to selectively erase specific knowledge from trained AI