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Field
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on classical networks, quantum networks are slowly becoming a reality. The coordination algorithms that govern their operation are unlike those employed in classical networks, necessitating novel verification
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optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware
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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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. The organisation is currently working on research project(s) related to the release of a new digital product that conducts optimisation of AI algorithms for sustainable home retrofitting solutions. Opportunity
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our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering Research groups at the Software Technology department under supervision of dr
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with the rest of the team, you will build demonstators for the Find2Fix technology at our industrial partners. You will work in the cyber analytics and CISE labs in the Algorithmics and Software Engineering
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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) to specify use cases from the perspective of the relevant domains as well as model transformation, analysis, and synthesis algorithms that provide functionally correct system configurations with a guaranteed
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., term rewriting) to algorithmic optimizations (e.g., group level algorithms), and to hardware optimizations (e.g., automated pipelining). The PhD student will be supervised by Nusa Zidaric. Key
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data