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to develop algorithms to factor linear differential operators using p‑adic techniques. The central question of this proposal concerns the efficient symbolic resolution of linear differential equations
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consensus. The successful candidate will develop the algorithmic foundations of this framework—making the motion-generation stack modular and heterogeneous (combining model-based control with learning-based
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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