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Field
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Experience Appropriate PhD in a related field. Preferred Qualifications Experience with machine learning and deep neural network techniques. Experience with wearable and sensors placed in the environment
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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(“overparameterized”) machine learning models, like probabilistic graphical models, deep neural networks, diffusion models, transformers, e.g. large language models, etc. SLT is based on the geometrical understanding
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the spatial reasoning capabilities of multi-mode large language models and hybrid AI systems combining artificial neural networks with symbolic AI. In-house research and development of one of these systems
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical
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, and observational measures, to assess bilingual infants’ dual language development during the first two years of life. The studies will target three core components: (1) neural maturation, (2
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convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya, S. & Pandey, M. (2024). PCFRIMDS: Smart Next-Generation Approach for Precision Crop and Fertilizer
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at the intersection of scientific computing and machine learning. At a high level, the project is to build neural network models of potentials that appear in Hamiltonians for time-dependent quantum systems. The postdoc
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) are looking to hire a post-doctoral associate to carry out joint research in resilience of neural networks, including large language models (LLMs) and other generative AI systems and foundation models
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, clustering, neural networks) for spectral interpretation, segmentation, and material identification. • Create and curate a dedicated HSI spectral library for cultural heritage materials, linking reference