PhysioNet Competitor (Biomedical Signal Processing & ML)

Executive Staff Recruiters / ESR Healthcare; United States; On-site; USD 83,000 - 165,000; Full time

Job summary

PhysioNet Competitor is seeking contributors with hands-on experience in processing biomedical signals and time-series data. You will design rigorous problems, implement reference solutions in Python or MATLAB, and tackle tasks spanning preprocessing, segmentation, feature extraction, and forecasting using state-of-the-art machine learning methods.

Strong candidates will showcase prior competition results or open-source contributions, and demonstrate a deep understanding of physiological signals

Qualifications

• Experience with physiological signals such as ECG, EEG, PPG, EMG or other clinical time-series data.
• Participation in PhysioNet/Computing in Cardiology Challenge or similar competition.
• Public evidence of work through official team results, papers, reports, or meaningful code contributions.

Responsibilities

• Create rigorous, self-contained problems involving biomedical signals, time-series data, signal processing and machine learning.
• Develop clear reference solutions using Python, MATLAB or similar computing environment.
• Work on tasks including signal preprocessing, filtering, segmentation, feature extraction, representation learning, classification or forecasting.

Skills

Biomedical signals

Time-series analysis

Machine learning

Signal processing

Python

MATLAB

Tools

Python

MATLAB

Job description

PhysioNet Competitor (Biomedical Signal Processing & ML)

Pay: $60-$120/hour

Location: Global, fully remote

Job Type: Contractor (~15 hours per week)

Schedule: Flexible-you choose the hours and days you work, including weekends if desired

We are looking for people who have worked directly with physiological signals and can turn noisy, real-world data into a rigorous and reproducible analysis. Strong candidates will be able to explain the methods they developed, the experiments they ran, the technical decisions they personally owned, and how they validated their results. No prior experience working on AI training projects is required.

Scope of Work

• Create technically rigorous, self-contained problems involving biomedical signals, time-series data, signal processing, and machine learning.
• Develop clear reference solutions using Python, MATLAB, or another appropriate scientific computing environment.
• Work with tasks involving signal preprocessing, filtering, segmentation, feature extraction, representation learning, classification, or forecasting.

Preferred Qualifications

• Participation in a PhysioNet/Computing in Cardiology Challenge or a comparable biomedical signal processing or scientific machine learning competition.
• Strong competition results and evidence of a substantive personal technical contribution.
• Public evidence of your work through an official team result, conference paper, technical report, open-source repository, or meaningful code contribution.
• Hands-on experience with physiological signals such as ECG, EEG, PPG, EMG, respiratory signals, polysomnography, or other clinical time-series data.

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