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Field
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learning libraries (such as TensorFlow, PyTorch), along with experience in structural modeling tools (e.g., Vienna, Rosetta, RNAstructure) and graph neural networks or transformers applied to molecular
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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, host genetics, and addiction vulnerability. The postdoctoral fellow will lead the development of cutting-edge, explainable graph neural network (GNN) models that integrate microbiome functional profiles
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and fabricating transparent and flexible microelectrode arrays (MEAs) based on conductive polymers for recording the electrical activity of enteric neuronal networks, as well as for performing cellular
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-Guachi, L., Gomez-Mendoza, J.B., Revelo-Fuelagan, J. & Peluffo-Ordonez, D.H. (2021). Enhanced convolutional-neural-network architecture for crop classification. Applied Sciences, 11(9), 4292. Bhattacharya
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implement AI/ML models (e.g., graph neural networks, transformer-based models) for retrosynthetic pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions
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computer science, or related fields to apply. A strong publication record is encouraged and previous experience in areas such as Neural network vulnerabilities and defenses, Anomaly detection, Adversarial machine
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. The postdoctoral fellow will lead the development of cutting-edge, explainable graph neural network (GNN) models that integrate microbiome functional profiles, host genetic variation, and behavioral phenotypes from
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imaging data (e.g., CellProfiler, convolutional neural networks).•Knowledge of chemo-informatics and drug discovery.•Strong practical statistical skills (batch effects, confounders, experiment design). You
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fundamental questions about the transcriptional regulation of inhibitory neuronal development and function in neural circuits, the role of cerebellar circuit dysfunction, and disrupted gene regulatory networks