Remote Foundation Models Intern

Summary

Join our team for an exciting internship focused on advancing the application of foundation models in the field of bioacoustics, with opportunities to explore transfer learning from state-of-the-art audio foundation models to bioacoustic tasks.

Requirements

• Currently pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field
• Experience in training and evaluating neural networks
• Proficiency in programming with Python and familiarity with machine learning libraries

Responsibilities

• Benchmarking existing audio foundation models on bioacoustics tasks such as BEANS, Perch, DCASE
• Training and fine-tuning foundation models on general-purpose audio data to improve downstream performance on bioacoustics benchmarks
• Experimenting with quick adaptation methods such as SLM (https://arxiv.org/abs/2310.00230) to bootstrap a bioacoustics foundation model
• Collaborating with the research team on applying the foundation model to current ESP projects

Preferred Qualifications

• Proficiency in deep learning frameworks (PyTorch preferred)
• Previous experience with bioacoustics dataExperience in training and fine-tuning large-scale foundation models (BERT, T5, HuBERT, LLMs, etc.)
• Publication in leading signal processing or machine learning conferences (e.g., ICLR, IJCAI, ICML, NeurIPS, ICASSP, Interspeech)

Benefits

Up to 10,000 USD a month, based on experience and qualifications