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Field
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investigation will be zero-knowledge succint non-interactive argument of knowledge-- ZkSNARK. Subsequently, we will formalise the front-end (R1CS) and back-end (Groth16) of ZkSNARK in the Coq theorem prover and
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providing a structured, semantic framework that enhances knowledge sharing and data reuse across different platforms and systems. Project Aim This PhD will develop an ontology-based methodology to improve
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verification of resilient state estimators for the eVTOL and HESS. Essential and Desirable Criteria - Background: control/mechanical/electrical engineering, physics or computer science - Essential knowledge
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degree in a relevant subject (physics, mathematics, engineering, computer science, or related subject) Proficiency in English (both oral and written). Knowledge in cryptography is desirable. Studentship
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experimental psychology and cognitive science to augment human intelligence with AI. Your experience and ambitions eligible for PhD study at Aalto University (https://into.aalto.fi/display/endoctoralsci/How
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, mathematics, engineering, computer science, or related subject) Proficiency in English (both oral and written). Knowledge in cryptography is desirable. Studentship and eligibility This studentship is suitable
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processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans
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solutions, focusing on integrity assurance for safety-critical applications. It addresses the growing need for cognitive navigation systems that are able to operate reliably with a diversity of dedicated
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Lancaster’s Security and Protection Science initiative (www.lancaster.ac.uk/security-and-protection-science ) and Security Lancaster (www.lancaster.ac.uk/security-lancaster ), one of the UK’s largest security
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront