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, driver’s license number, social security number, passport number, and contact details), education information, financial information (for payroll and benefits), and professional and employment history
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well as security penetration testing. The primary responsibility of this role is to deliver on an industry innovation research project where you will be part of a research team to investigate and determine
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challenge of enabling ultra-reliable low-latency communications (URLLC) with priority-aware offloading for public safety services. The successful candidate will be responsible for the end-to-end investigation
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, and delivery, ensuring milestones are met and solutions meet industrial standards for quality and safety. Requirements: PhD in Mechanical Engineering, Nanotechnology, or a closely related discipline
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before appointment, which will be conducted by an external background screening service, in compliance with the European Space Agency's security procedures. The information published on ESA’s careers
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experienced working on industry problems /across sectors. Focus areas for applied AI include domains such as: Advanced Manufacturing & Semiconductors Urban Systems AI-Augmented Engineering AI Safety & Security
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for public safety services. The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted computation offloading strategies that leverages edge intelligence. The role
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, where applicable, reimbursement of contributions to the Voluntary Social Security scheme, pursuant to the Research Fellowship Holder Statute and the Research Fellowship Regulations of Universidade NOVA de
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Conduct research in adversarial machine learning, AI security and the robustness of deep
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, manipulation and adaptive gripping mechanisms, baggage handling and stacking systems, safety, and reliability systems. Develop CAD models, digital simulations, physics-based modelling, concept evaluation