Research Assistant, ECECS
Position Type: Federal Work Study or Non-Work Study
Essential Duties and Responsibilities
Responsibilities include, but are not limited to the following:
As a Research Assistant in the field of Machine Learning-Based Indoor Localization, your responsibilities will include:
Data Collection and Preprocessing:
- Collecting Wi-Fi RSSI (Received Signal Strength Indicator) fingerprints in various indoor environments using appropriate tools and equipment.
- Organizing and cleaning collected data to ensure accuracy and consistency.
Data Annotation and Labeling:
- Annotating collected data with ground truth location information for training and evaluation purposes.
Algorithm Development:
- Assisting in the development and optimization of machine learning algorithms for indoor localization based on Wi-Fi RSSI data.
- Conducting experiments to evaluate the performance of different algorithms and fine-tuning parameters.
Literature Review:
- Conducting literature reviews to stay up-to-date with the latest research in the field of indoor localization and machine learning.
Documentation:
- Documenting research methodologies, findings, and experimental results in an organized and clear manner.
- Preparing reports, presentations, and visualizations to communicate research progress.
Requirements
Minimum Qualifications:
- Enrollment in a relevant undergraduate or graduate program, such as Computer Science, Electrical Engineering, or a related field.
- Strong interest in machine learning, wireless communication, and indoor positioning systems.
- Knowledge of programming languages such as Python, and familiarity with machine learning libraries (e.g., TensorFlow, PyTorch).
- Excellent analytical and problem-solving skills.
- Strong communication and teamwork skills.
- Attention to detail and ability to work independently.
Anticipated Schedule:
- Monday and Wednesday from 11 am to 3 pm.
Job Contact:
- Shadi Bani Taan – banitash@udmercy.edu – (313) 993-1163