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Field
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learning and sequential sampling models. Develop methods that connect agent behavior with human behavior at the level of mechanism. Use simulation, recovery analyses, and out-of-sample prediction
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aerodynamic prediction methods and aeroacoustic models. Your research will focus on three interconnected areas: High-fidelity aerodynamic optimization: Develop a design tool combining adjoint-based optimization
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machine-learning methods to weather forecasting, climate analysis, or spatial data. Strong background in spatiotemporal modeling, probabilistic prediction, or time-series analysis. Proficiency in Python and
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predictive control (MPC) Proficiency in simulation tools: MATLAB/Simulink Skills in constrained optimization Experience in modeling and simulation of complex robotic systems Demonstrated ability to produce
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to investigate severe weather hazard activity in large ensemble simulations. Evaluate model biases, e.g. from GFDL’s Seamless System for Prediction and EArth System Research (SPEAR) large ensembles. Identify and
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of statistical and machine learning methods to Child and Adolescent Needs and Strengths (CANS) and other Transformational Collaborative Outcomes Management (TCOM) assessment data, including predictive modeling
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for the S2S timeframe with an emphasis on generative AI techniques, evaluating such models, and working with a team of scientists interested in pushing the boundary of predictability. Position Requirements
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Weather and climate prediction are undergoing a profound transformation. Alongside traditional physics-based forecast systems, machine-learned (ML) weather prediction models, hybrid ML-physics
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 12 days ago
transformed into heterogeneous graphs, where rows become nodes and primary–foreign key relationships induce edges. GNNs can then propagate information across the relational structure and learn predictive
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models; predicting density and uncertainty surfaces from those models; evaluating predictions with contemporaneous data not included in the models as well as knowledge from the literature; and