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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
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The goal of this project is to study the anatomical characteristics of the neural
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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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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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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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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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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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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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. 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