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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
Large language models (LLMs) can read and write text and code, call tools, and follow instructions. They now allow us to build agents that plan and act over many steps instead of giving a single
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Primary supervisor - Prof Mark Searcey One of the key problems in the development of new anticancer agents is specificity. How do you get the compound to the site of action in the body and avoid
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fields: nonlinear PDEs, functional analysis, numerical analysis, PDE-based modeling and numerical simulations excellent command of the English language ability to teach in German and English We offer
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background in (not necessarily all) the following fields: nonlinear PDEs, functional analysis, numerical analysis, PDE-based modeling and numerical simulations excellent command of the English language ability
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characterised at atomic resolution via crystal structures of PrfA–peptide complexes. Building on these findings, this PhD will develop peptide-based PrfA inhibitors as novel anti-Listeria agents. The project will
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, Attacks, and Defenses”. Your work assignments Large language model (LLM) agents represent the next generation of artificial intelligence (AI) systems, integrating LLMs with external tools and memory
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of new steel grades. To this end we will design agentic multi-modal models, whose components will be trained on legacy data, and which can be queried to provide a prompt-based summarization of relevant
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computable general equilibrium modeling, led by five team members. The first three pillars concern the development of computational agent-based models to explore three different channels of risk propagation
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to agent-based and computable general equilibrium modeling, led by five team members. The first three pillars concern the development of computational agent-based models to explore three different channels
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sciences, economics and regulation. Job description The project of the PhD student based at CWI in Amsterdam will focus on techno-economic models (and in particular multi-agent modeling) of energy exchange