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in, for example, a university or another expert organization. You will work in an international community with researchers and various stakeholders, both face-to-face and via digital channels, so
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work by further developing the code to include time dependent cohesive bonding between the ice blocks. Then you will use the code to model ice pile-up. The work will have a multi-scale component as you
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(AWSoM/IA) to model stellar coronae and winds driven by dynamo‑generated surface magnetic maps. Disseminate results in journals and conferences; contribute to open‑science releases of data and code. In
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, deepfake detection, and generative audio. A strong open-science environment: the project aims to publish in leading venues and release software source code and trained models to support reproducibility and
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reports, simulations, analysis, code samples, or documentation (multiple references are considered beneficial). The above duties are not exhaustive; the PhD candidate may be required to undertake other
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sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting and analytics, and are keen to push the boundaries of data-driven
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science who are curious about applied machine learning in the natural sciences. Prior machine learning or Python experience is a strong bonus, but not a must. We seek colleagues who enjoy coding, scripting
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or similar. Running social simulations with AI agents using platforms like Concordia or similar. Coding in Python or a similar language for simulation, visualization, and advanced statistical analysis (causal
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implementation in Fire Dynamics Simulator CFD code Experimental validation of simulations Machine learning for accelerated evaluation of radiative properties The doctoral degree will be awarded upon successful
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models for gases to solid particles Experimental characterization of fuel particles using FTIR Mathematical model formulation and programming Model implementation in Fire Dynamics Simulator CFD code