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We invite you to apply for a 2-year postdoctoral position in the area of hybrid AI, combining modern language models (LMs) with ontologies and knowledge graphs (KGs). The position is part of a newly
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Research Associate position. The successful candidate will lead the development of ocean models, building upon existing modeling frameworks established by the group. The primary modeling tool will be SCHISM
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 days ago
applied mathematics, control, or related fields, with knowledge in several of the following areas: Graph theory and network modeling Dynamical systems and physical modeling (ODE/PDE, multi-agent systems
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synthesizing findings with prior knowledge from the scientific literature, a process that today depends heavily on manual expert interpretation. Recent advances in large language models, agentic systems, and
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, particularly for multi-variable simulations. Knowledge of complex systems modeling applied to urban dynamics. Publications in scientific journals. Personal and Organizational Qualifications: Ability to develop
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multi-scale modeling across spatial and temporal domains, including residual stress and distortion in large-format structures Experience with CAD/CAM, MATLAB, G-Code Knowledge and/or experience with solid
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requires experience in cognitive processes underlying food choice, context effects, experimental research, quantitative analysis, computational modeling, and eye tracking. Applications must be submitted by
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of Biostatistics at Brown University is seeking a highly motivated individual for a postdoctoral research associate position interested in developing statistical methods for modeling medical imaging data
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commensurate with abilities and experience Type of Position Other Position Time Status Full-Time Required Education PhD in Engineering Required Related Experience see Skills / Knowledge / Abilities Required
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, including direct knowledge of its Julia-based modeling environment, software architecture, domain-specific modules, and underlying computational infrastructure. ● Prior hands-on experience contributing