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to facilitate the management and editing of the different specialist agents that make up the system. - Implementing mechanisms to generate artificial conversations, based on historical case studies, for training
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. • Familiarity with agent-based and compartmental models for infectious diseases. The grade of appointment will be accorded based on candidate’s academic qualifications and years of relevant experience. Applicants
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plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep
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for the design and development of peptide-based nanocarriers for the controlled and targeted delivery on anti-inflammatory and anti-bacterial therapeutics upon injection of the hydrogel. The Research Fellow will
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mathematics) or should be close to submitting their PhD thesis. They should also have experience in handling longitudinal data and conducting microsimulation or agent-based modelling. Good communication and
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in sensitive contexts, such as cybersecurity. Then, based on the knowledge acquired, they should conduct state-of-the-art research on defence mechanisms, methodologies, and tools, assessing
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on the development of a 4D injectable hydrogels for the treatment of deep wounds; so-called tunnel wounds. The CRMD team will be responsible for the design and development of peptide-based nanocarriers
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. Proven expertise in AI/ML for systems or hardware co-design, including use of reinforcement learning, LLMs, graph-based optimization, or agentic AI. Familiarity with security concepts and cryptographic
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: Recommender system for biological control agents against plant pathogens”, “2023.14580.PEX”, ”DOI: https://doi.org/10.54499/2023.14580.PEX ”, funded by the Fundação para a Ciência e a Tecnologia, I.P. through
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a track record in computational modelling that explores the dynamics of AI systems and the development of autonomous AI agents, experience with machine learning, reinforcement learning, and generative