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security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and suggested patches, easier to understand using interpretable AI using state machine
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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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tool that allows developers to quickly find and fix software errors including security vulnerabilities. You will innovate the Find2Fix pipeline by making the different steps, including found issues and
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engineering starts from use cases (typical and exceptional) and various system scenarios (different operating modes, failures). This will require the development of suitable domain-specific languages (DSLs
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of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations
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work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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. Your work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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, machine learning or similar. Alternatively, you have gained essentially corresponding knowledge in another way. The applicant is expected to have good knowledge of computer science, mathematics, algorithms
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coding the appropriate algorithms and methods that implement the novel concepts and model. Gathering experimental or observational data to test hypotheses or highlight the strength and boundaries
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exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised