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Field
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coefficient modeling. Experience in integrating modeling with leaching and separation processes for scale-up. Advanced data interpretation using electron diffraction, dendritic growth modeling, and REE behavior
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or Postdoctoral position (m/f/d) - Interpretable Machine Learning for Catalytic Reaction Network Discovery. A full-time PhD or Postdoctoral position is available in a collaborative Max Planck research
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either or both agent-based modeling and social network analysis/network science. The ideal candidate should be able to program in C or C and have experience working with tera-bytes of data. Preference
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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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in this kind of environment will result in models too large to be handled and too instable to be solved. Data-driven approaches need to be used in addition to enrich the physics-based models
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Post-doctoral Position in AI Causal models for Synchrotron Anomaly Detection H/F This post-doctoral position is part of a collaboration between LIAD (Laboratory of Artificial Intelligence and Data
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We are seeking to appoint a Researcher in Infectious Disease Modelling. You will be an integral part of the Pathogen Dynamics group at Oxford, led by Christophe Fraser, where the team focuses
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level of detail extracted from these experiments. As part of this role, you will work closely with other researchers to translate these experimental results into our numerical models, helping to improve
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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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researchers to translate these experimental results into our numerical models, helping to improve their predictive capability. You will help ensure a healthy and vibrant research environment within the Impact