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or graph neural networks proficiency in Python and related computational toolchains a strong interest in interdisciplinary research bridging project governance, systems thinking, and intelligent control
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real-world applications in green chemistry and industrial synthesis. Key Responsibilities: Develop and implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic
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include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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projects ranging from score-based generative models, energy-based models, Bayesian analysis of graph and network structured data, highly multivariate stochastic processes; with data applications ranging from
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include: · building hierarchical causal graphs to account for the multi-scale structure of the experimental system, · detecting latent variables that may affect causal inference
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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investigator and Postdoctoral scholars/fellows on the status of research. Collect and log laboratory results, clinical outcomes and/or survey data. Evaluate and perform data analysis using graphs, charts
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Information Modeling (BIM), including Revit, IFC, ifcopenshell, compliance checking, design generation, and design checking; and Experience in AI and LLM-related development, including RAG, knowledge graph
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, computational fluid dynamics and material science, dynamical systems, numerical analysis, stochastic problems and stochastic analysis, graph theory and applications, mathematical biology, financial mathematics
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graph theory. Qualifications Candidates with a Ph.D. in any area of cognitive neuroscience broadly defined (e.g., Psychology, Neuroscience, Computer Science, or a related field) are welcome to apply