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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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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
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Language Model (LLM) Strong biostatistics knowledge including survival analysis and causal inference Experience with reinforcement learning, agentic AI systems and autonomous decision-making frameworks Data
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agents and treatment combinations in cancer models. Manage and maintain mouse xenograft models (including PDX and cell-line derived models). Perform drug administration, tumor measurement, sample
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responsibilities include: Generation of phenotype-specific networks from bulk-RNAseq and scRNAseq data from rare disease patients Building executable models (Boolean, ODE, agent-based or others) from omics data