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Field
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skills and familiarity with LLM APIs (e.g., OpenAI API), agent frameworks (e.g. LangChain), PyTorch, and the Python scientific stack (e.g., numpy, pandas, scikit-learn). Experience with front-end
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performance for data throughput and latency Extend functionality to interact with Python or REST APIs Interface with collaboration partners and participate actively in a collaborative work environment to turn
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custom tools and end effectors for robotic assemblyGood knowledge of the CAD software Rhinoceros 3D, its plugin Grasshopper 3D, and its API RhinoCommonReal-world architecture and/or construction
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to enable downstream APIs and feature services Develop ETL workflows for cross-domain spatial data integration using staged refinement (e.g. Medallion Architecture) Engage with UF faculty leading FLDT
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developing LLM-based applications using Python APIs. Experience with large scale molecular dynamics (MD) packages e.g. lammps Experience with version control (e.g., Git) and collaborative software development
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, NetworkPolicies, Pod Security, service-mesh mTLS such as Istio/Linkerd). Experience implementing/assessing identity and access for xApps/rApps and SMO northbound APIs (OAuth2/OIDC, JWT, fine-grained policy), and
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University Chicago, in partnership with members of the Learning Theory team at Google. More information about IDEAL can be found here: https://www.ideal-institute.org/ . The positions will be hosted by
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custom tools and end effectors for robotic assemblyGood knowledge of the CAD software Rhinoceros 3D, its plugin Grasshopper 3D, and its API RhinoCommonReal-world architecture and/or construction
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, crop yield). -Familiarity with geospatial data and tools (e.g., GIS, QGIS, Google Earth Engine). -Knowledge of explainable AI (e.g., SHAP, LIME), model interpretation, and/or uncertainty quantification
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design and testing and chassis to system design; The FPGA gateware design and verification for qubit control signal processing on different FPGA chips; The low-level software driver and API development