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rigs" for components such as intake, nacelle, bypass duct as well as nozzle and mixer. Implementation & training: you prototype selected AI/ML models (e.g. PINNs, graph-based models) within
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, such as books and calculators for students. Demonstrate the use of calculators, both graphing and non-graphing, and any software used in the STEM courses. Assist in compiling end-of-semester statistical
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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material science knowledge into deep learning models such as Large Language Models (LLMs). More concretely, our project include introducing material science knowledge into knowledge graphs and conducting
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: • Multimodal data integration: combining structured data (graphs, ontologies, relational databases), unstructured text (scientific literature, clinical reports), and molecular or numerical features into a
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cluster, ensuring that the data are in the right format, analyzing data using appropriate tools, and presenting the results in readable formats such as graphs and tables, and saving the results in a
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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: Familiarity with ImageJ, Fiji, or similar image analysis tools.Data analysis: Basic skills in Excel, Prism, or R for graphing experimental data.Independence: Proven ability to follow complex protocols with
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on priorities, and work both independently and as part of a team. PREFERRED: experience with molecular machine learning, biological data, chemical data, clinical data, materials data, graph-based methods
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ability to achieve certification within 1 year of employment. Knowledge / Skills / Abilities Knowledge Knowledge of record-keeping procedures. Knowledge of how to create tables, charts, and graphs