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PhD Studentship: Designing Human-AI Teams for Meaningful Human Control of 'Machine-Speed' Operations
January 2027. This PhD explores the design of human-AI teams when the AI agents operate at ‘machine-speed’ (i.e., significantly faster than human cognitive and communication capabilities). The research will
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as part of a team translating psychological constructs into mathematical/dynamical rules driving comprehensive agent-based models incorporating disease transmission, behaviour and feedback loops
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the field of Computation Applied to Environmental Sciences and Engineering and Sustainability, in the scope of the R & D Unit “Centro de Investigação em Ambiente e Sustentabilidade” (CENSE) (UID/4085/2025
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supervisors. A wide variety of PhD projects is available; example project areas are: Human-machine teaming AI agents for air traffic control Weak signal analysis for air traffic control room situational
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contrato a termo incerto, de um(a) Investigador(a) Doutorado(a), com a referência interna #NOVAID293, para o exercício de atividades de investigação na área de Ciências do Ambiente ou área afins, no âmbito
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from Amsterdam's network of shaded routes and heat shelters to Zaragoza's xerophytic "climate oases"—alter the outdoor thermal conditions and directly minimize ambient heat exposure for urban populations
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clinicians. The research will involve investigating fungal cell biology, mechanisms of action of novel antifungal agents, mechanisms of antifungal resistance, and host-pathogen interactions involving A
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Inria, the French national research institute for the digital sciences | Rennes, Bretagne | France | about 1 month ago
preserved at ambient temperature provided it is protected from light and humidity. Goal The goal of the project is to develop an algorithm to allow robust retrieval of data in the context of DNA-based data
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a major scientific and societal challenge. It requires fundamentally new approaches to control and optimization in environments where multiple agents interact under uncertainty and constraints. In
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& Agentic AI (A3) Lab, supervised by Associate Professor Behzad Bozorgtabar and co-supervised by Professor Qi Zhang. Research vision How can intelligent robots understand instructions, anticipate