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Postdoctoral Associate to investigate the neural mechanisms underlying continual learning in humans. The successful candidate will develop computational models examining the tradeoff between task
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a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian-Lagrangian (CEL), Material Point Method
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well as market and organization considerations. Education: Ph.D. in machine learning, computer science, engineering, science or related technical discipline. Experience: Expertise in developing and training AI
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via autophagy and lysosomal targeting in learning and memory and disease models using rodents and iPSC-derived cell cultures. These mechanisms will be investigated in both healthy conditions and
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ecological systems with frequency-dependent selection. Planned projects use dynamical systems, stochastic differential equations and agent-based models, statistical methods for parameter inference, network and
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optimize machine learning (ML) and AI-driven climate risk models for the ClimateIQ project. This position will refine ML approaches to provide high resolution climate hazard information and improve decision
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and multimodal perception/behaviors, generative diffusion models, cloud-based computer graphics, and neural rendering (e.g., neural radiance fields and 3D Gaussian splatting). The NYU ICL
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/photometric data analysis, and spectral modelling, are strongly encouraged to apply. Successful candidates will be encouraged to develop independent projects in addition to supporting the research of the PI
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Details Posted: 31-Mar-25 Location: New York, NY Categories: Academic/Faculty Internal Number: 165613 We are seeking an outstanding scholar with expertise in hydrological and hydrodynamic modeling
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at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post