PhysioNet Competitor (Biomedical Signal Processing & ML) | Remote - contract Remote (International)

Dover; India; À distance; INR 5 233 000 - 7 850 000; Temps partiel

Résumé du poste

Dover is seeking a PhysioNet Competitor (Biomedical Signal Processing & ML) for a fully remote, contractor role. Work approximately 15 hours per week with a flexible schedule, contributing to advanced biomedical signal problems, reference solutions, and ML models to improve AI systems.

Ideal candidates have PhysioNet Challenge experience or comparable competitions, with hands-on work on ECG, EEG, PPG, or other time-series data, and a track record of clear methodological explanations and

Qualifications

• Experience with physiological signals and biomedical data.
• Ability to explain methods, experiments and validation clearly.
• Strong background in signal processing and ML for time-series.

Responsabilités

• Create technically rigorous, self-contained problems involving biomedical signals and ML.
• Develop reference solutions using Python, MATLAB, or similar environments.
• Handle tasks on signal preprocessing, feature extraction, classification or forecasting.

Connaissances

Biotechnology

Biology

Machine Learning

Description du poste

Role Title: PhysioNet Competitor (Biomedical Signal Processing & ML)

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 engaging specialists with experience in the PhysioNet Challenge or comparable biomedical signal processing and machine learning competitions. In this role, you will apply your scientific programming, signal processing, and modeling expertise to create, solve, and evaluate advanced technical problems that help improve next-generation AI systems.

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.

Required Skills

• Biotechnology

• Biology

• Machine Learning

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.

Process

• Apply to the role and complete the screening questions.

• Complete an AI interview of approximately 30 minutes.

• Complete the hiring manager review.

Compensation Structure

• Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.

• Minimum submission requirements apply.

Start Timeline & Availability

We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first task within 24–48 hours of completing onboarding.