InternshipAt — Central Institute of Mental Health
This is for CST394 Students ONLY
This opportunity offers hands-on experience in dynamical systems reconstruction (DSR) using recurrent neural networks (RNNs) as flow operators. Students will work on applying machine learning models to understand and replicate the behavior of complex time-dependent systems, with a focus on practical implementation and evaluation.
By the end of this opportunity, students will understand the core principles of DSR and how machine learning models can be used to learn from time series data to provide scientific insight into the underlying data generating processes. They will gain practical experience in applying a specific class of RNNs to both synthetic and real-world datasets, and will contribute to developing new approaches for addressing current challenges in the field.
Students will receive training in applying RNNs as flow operators for DSR. This includes instruction on the structure and function of relevant RNN architectures, model training and evaluation strategies, and techniques for interpreting and improving model performance on diverse time series data.
Students can contact the supervisor directly