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of the following areas: Wireless and satellite communications AI/ML for dynamic networks including Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models
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research on memory and behavior is how the brain responds to environmental stimuli, and a major challenge here is the heterogeneity of cell-signaling pathways, brain cells and neural networks. We study
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epilepsies. They use a range of advanced genomic techniques including single-cell and spatial multiomic evaluation of epilepsy surgical tissue as well as iPSC-derived neural cultures and mouse models
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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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that methodological advances are developed with direct translational and scalability considerations. Responsabilities: Lead the development of hybrid foundation model-graph neural network architectures for gene
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communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL - Quantum Reinforcement
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
immunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden
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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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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