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Python, PyTorch, and Linux/command line Familiarity with LLM in-context learning and prompt engineering Basic understanding of modern LLM models, ecosystems, and pipelines, including retrieval-augmented
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lung tumors. We utilize a multifaceted approach that encompasses preclinical studies in immunocompetent syngeneic and genetically engineered co-mutational models of KRAS-mutant NSCLC as well as PDX/CDXs
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
cutting edge machine learning techniques for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable
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this project, the candidate will be expected to explore a range of fundamental and applied problems in mathematical modelling of plant morphogenesis based on these approaches. Depending on their preferences and
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of thermodynamics, HVAC systems, or control systems is advantageous. Experience with environmental monitoring equipment, sensor systems, or energy modelling software will be beneficial. Effective communication skills
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Engineering. Experience with MLflow, Feature Store, Model Serving, or AI workflow orchestration. Experience with vector search, embeddings, or LLM-based application components. Familiarity with machine learning
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welcome: Real-time or streaming pipelines Control / robotics / feedback systems Optical systems / optical tweezers / laser-based manipulation Probabilistic modelling / tracking Software in the loop (SiL
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robust control methods Controllers for underactuated and coupled robotic dynamics Stability-aware and model-based autonomy ROS/ROS2-based autonomy development Real-world testing on aerial, underwater, and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 4 days ago
geophysical research. Experience in GIS-based mapping, tectonic modeling, and/or research relevant to the outer planets satellites is highly desirable. Field of Science: Planetary Science Advisors: Robert
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic