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Field
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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modeling software. Proficiency in chromatographic and FTIR analytical techniques. Skilled in analytical methods for catalytic/separation agents materials characterization. Solid understanding of chemical
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Experience with methods such as Stochastic Actor-Oriented Models (SAOM) or agent-based modeling (ABM) is an advantage but not required What We Offer A supportive, lively research environment with international
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with simulation modeling (e.g., discrete-event, agent-based, systems dynamics). Special Instructions to the Applicants: The Ph.D. must be conferred before the commencement of employment in
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. The preferred candidate will have a strong academic or industrial background in machine learning, trustworthy machine learning and AI, agentic AI, adversarial machine learning, graph-based learning, multi-domain
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multi-agent pathfinding (MAPF) algorithms - Experience across multiple areas is a strong plus; Experience developing ML-based optimization approaches is a strong plus; A strong publication track record is
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of computing and healthcare. Methodologies of interest include: Multi-modal learning Foundation models, including large language models Agentic AI Multi-agent AI systems Transfer learning Self-supervised
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, agent-based modeling, active transport modes mobility, and urban accessibility assessment models; c) In case the applicant is not a native speaker of Portuguese or English, they must have language skills