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and topological machine learning; AI-assisted theorem proving, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed
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group insurance; 5. Extra social benefits. Selection process The first step of the competition procedure is the formal verification of the submitted documents. The applications which are successfully
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faculty and researchers across disciplines. The successful candidate will contribute to one or more of the following components: Reinforcement Learning for Reasoning Formal Verification and Lean Automation
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faculty and researchers across disciplines. The successful candidate will contribute to one or more of the following components: Reinforcement Learning for Reasoning Formal Verification and Lean Automation
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, state estimation, formal methods and system verification, distributed or multi-agent systems, and data-driven analysis of dynamic systems. • Previous research or project experience in one or more of the
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least a doctoral degree; - have relevant scientific achievements; - take an active part in scientific life. Selection process The first stage of the competition procedure is the formal verification of
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software requirements Formal methods for software systems and software architectures AI-Driven Software Development and Assurance AI-driven software development processes Verification of AI systems
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Tenure-Track Assistant Professor / Associate Professor in AI-Driven SW Specification, Development...
, Development, Verification and Validation. The targeted starting period is Spring 2027, with room for flexibility. The successful candidate will join the SustainAI – Sustainable and AI-Driven
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, quality control and maintenance functions, and results reporting and verification. Participates in training and in-service programs to maintain and upgrade competency at the level required by the
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theorem proving, formal verification, proof assistants, automated proof search, and AI-guided mathematical discovery; neural operators, physics-informed learning, inverse problems, uncertainty