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Scaling Synthetic Data Generation for Foundation Models in Prognostics and Health Management
We are offering a paid internship opportunity at the EPFL IMOS lab to explore innovative data generation techniques that enhance the capabilities of Foundation Models. In this role, you will investigate synthetic data creation for Prognostics and Health management (PHM) scenarios, working towards pretraining a foundation model for PHM and scaling synthetic data generation to millions of datasets. You’ll gain hands-on experience with cutting-edge Machine Learning tools, collaborate with researchers, and help shape the future of data-driven PHM. If you're eager to take on the challenge of scaling data generation for Foundation Models, we’d love to hear from you!
Keywords: Machine Learning, Deep Learning, Foundation Models, Synthetic Data Generation, Simulation, Prognostics and Health management (PHM)
Requirements
We seek a motivated candidate with a strong background in Machine Learning, Data Science, and Computational Methods. Proficiency in Python and experience with key libraries such as NumPy, SciPy, and PyTorch are essential. The candidate should have expertise in handling large-scale datasets, including efficient data storage and parallelized preprocessing. Additionally, experience in distributed computing, cloud infrastructure, and advanced optimization techniques such as mixed-precision training and multi-GPU acceleration is desired.
Interested students should submit an updated CV along with transcripts of academic records.
Requirements We seek a motivated candidate with a strong background in Machine Learning, Data Science, and Computational Methods. Proficiency in Python and experience with key libraries such as NumPy, SciPy, and PyTorch are essential. The candidate should have expertise in handling large-scale datasets, including efficient data storage and parallelized preprocessing. Additionally, experience in distributed computing, cloud infrastructure, and advanced optimization techniques such as mixed-precision training and multi-GPU acceleration is desired.
Interested students should submit an updated CV along with transcripts of academic records.
Not specified
Please contact me via E-Mail: raffael.theiler@epfl.ch
Please contact me via E-Mail: raffael.theiler@epfl.ch
