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of you Required PhD in machine learning, physics, or a related field. Established expertise in deep learning (familiarity with graph neural networks, transformers, diffusion and flow based generative
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main goals: building and using cohomological field theory (CohFT) structures on logarithmic Gromov-Witten invariants; understanding the geometry of the moduli space of genus 2 curves/abelian surfaces
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translating natural language specification into a symbolic representation (e.g. knowledge graph (KG) or logic program) and a symbolic solver computing the solution. Another example is the generation
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Are you passionate about mathematical control theory and its applications? Are you eager to develop estimation and control algorithms for large-scale dynamical systems with the aim of making next
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looking for the connection between theory and practice. For more information, please visit our website .
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-time, three-year position within the audio and speech processing team of the Signal Processing Systems (SPS) Section. In the SPS section, we conduct research in array signal processing, graph signal
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these solutions against explicit, formalized constraints by symbolic reasoning. An example is a LLM translating natural language specification into a symbolic representation (e.g. knowledge graph (KG) or logic
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experimental probes as neutron and X-ray scattering, as well as solid state NMR and computational approaches including density functional theory calculations. The direct access to these facilities and
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the gap between fascinating theory and real-world applications. Our current research in quantum computing spans several exciting directions, and you will have the opportunity to contribute to all of them
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, which allow for storytelling like comics to have complex sequential relationships. This work will require students to blend linguistic theories and psychological research on visual narrative sequencing