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candidate with a PhD degree in computer science, physical chemistry, chemical physics, theoretical physics, or a related field, with a strong background in training GNN-based ML predictive models (Equiformer
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. Proficiency in programming and data analysis. Experience in imaging/microscopy is an asset. Autonomy, rigor, creativity, team spirit, and strong communication skills. Additional Information Selection process
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