acme
We are seeking contributors experienced with advanced AI techniques to support ongoing work related to:
- AI EEG-based emotion recognition for AI-powered music recommendation
- Forecasting and time series analysis using modern approaches
Role Overview
You will help design, experiment with, and refine machine learning and large language model (LLM)-driven methods that connect neurological/emotional signals and music preferences, as well as improve forecasting pipelines.
Responsibilities
- Apply large language models to research and experimentation workflows
- Explore and implement few-shot learning techniques for limited-data scenarios
- Collaborate on AI EEG-based emotion recognition experiments for music recommendation systems
- Contribute to forecasting and time series analysis approaches in related projects
- Document methods, experiments, and findings in a clear, reproducible manner
Nice-to-Have Experience
- Working with EEG or other biosignal data
- Building or evaluating recommendation systems (especially for music or media)
- Hands-on experience with time series forecasting frameworks or libraries
How to Get Involved
If you have experience with large language models, few-shot learning, or related AI workflows and are interested in these projects, please review the project links below and indicate where you believe you can contribute most effectively.
Relevant Projects:
〔AI〕EEG-Based Emotion Recognition for AI-Powered Music Recommendation
〔Forecasting〕Time Series Analysis: Modern Approaches to Forecasting [wrap=info]
Notes: This role is focused on research and experimentation around AI EEG-based emotion recognition for music recommendation and modern forecasting/time series analysis methods.
Highlights:
- Location: Acme Township, Michigan
- Employment Type: Full Time
- YOE: NA
- Salary: NA