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Field
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, or materials informatics. Familiarity with explainable AI or counterfactual explanation methods. Experience with molecular dynamics data, graph neural networks, or multi-component system modelling. Track record
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will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include Advancing equivariant neural network potentials
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rendering, Gaussian splatting, etc.). Knowledge of graph neural networks. Experience in deploying and fine-tuning DL models, also large language or vision-language models. Practical experience on deploying ML
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research, all with the goal of improving human health. Aligned with Rutgers University–New Brunswick and collaborating university wide, RBHS includes eight schools, a behavioral health network, and five
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simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative
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optimization. AI/ML methods development: Neural networks, graph neural networks (GNNs), generative AI, or active learning for materials applications. Integration of theory and experiment: Using computation and
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on Nanoparticles You will develop atomistic models and machine-learning potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network
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, and similar equipment. Proficiency in Python programming including ability to install and use spiking neural network simulators such as SNNTorch, NEST, Nengo, etc. Experience with semiconductor memory
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records in hydrology, AI/ML, or water resources engineering. Preferred Qualifications Experience with: LLMs, graph neural networks, transformers, or physics-informed neural networks (PINNs). Cloud computing
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methods (e.g., PCA, PLS-DA, clustering, neural networks) to enable automated, polymer-specific classification. Optimize workflows for high-throughput imaging and real-world sample variability, minimizing