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Open nowFirst seen 3 days ago

Remote | PhysioNet Competitor (Biomedical Signal Processing & ML) — $60–$120/hour

24-MAG343 open roles

Where
New York, United States, Remote
Work mode
Remote
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Your applicationOpen nowRemote | PhysioNet Competitor (Biomedical Signal Processing & ML) — $60–$120/hour24-MAG · New York, United States, Remote

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. 24-MAG postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 58.1%60 days
This job: first seen 3 days ago

24-MAG median: 1 days open

The posting

We are sharing a specialised remote consulting opportunity for experienced Biomedical Signal Processing and Machine Learning Professionals with strong expertise in physiological time-series analysis, scientific programming, computational modelling, and biomedical data science to contribute their technical knowledge to advanced AI training and evaluation initiatives.

Selected professionals will develop technically rigorous biomedical research problems, create reproducible computational solutions, and evaluate complex signal processing and machine learning methodologies. The work combines practical experience from PhysioNet Challenges or comparable scientific competitions with physiological data analysis, algorithm development, experimental validation, and structured technical evaluation to help improve the accuracy, reasoning, and reliability of advanced AI systems. Prior experience with AI training projects is not required.

Key Responsibilities

Biomedical Signal Processing & Physiological Data Analysis

  • Analyse complex physiological signals, including ECG, EEG, PPG, EMG, respiratory signals, and other clinical time-series data
  • Develop technically rigorous research problems reflecting real-world challenges in biomedical signal processing
  • Evaluate physiological datasets for signal quality, noise characteristics, variability, and analytical suitability
  • Apply appropriate signal processing methodologies to extract meaningful information from complex biomedical recordings
  • Translate practical biomedical data challenges into clearly defined computational and scientific evaluation tasks

Signal Preprocessing & Feature Engineering

  • Develop and assess signal preprocessing workflows involving filtering, segmentation, normalisation, and artefact handling
  • Apply numerical and statistical methods to improve the interpretability and analytical quality of physiological signals
  • Design feature extraction and representation learning approaches for biomedical time-series applications
  • Evaluate the impact of preprocessing decisions on downstream modelling performance and analytical conclusions
  • Create reproducible workflows that demonstrate appropriate methodological choices and scientific reasoning

Machine Learning & Computational Modelling

  • Develop reference solutions using Python, MATLAB, or other appropriate scientific computing environments
  • Apply machine learning techniques to biomedical classification, forecasting, pattern recognition, and signal interpretation tasks
  • Evaluate model design choices, feature representations, performance metrics, and experimental assumptions
  • Develop computational approaches to challenging problems involving noisy, high-dimensional, or variable-length physiological data
  • Document modelling decisions, implementation details, and validation procedures to support reproducible scientific analysis

AI Training & Technical Problem Evaluation

  • Create self-contained scientific problems involving physiological signals, signal processing algorithms, and machine learning methodologies
  • Develop accurate reference solutions supported by clear computational steps, technical explanations, and reproducible results
  • Review AI-generated scientific analyses, programming solutions, and methodological explanations for correctness and completeness
  • Identify errors in technical reasoning, signal processing assumptions, algorithm implementation, and result interpretation
  • Provide structured feedback to improve AI performance across advanced biomedical computing and scientific problem-solving tasks

Scientific Validation & Research Quality Assurance

  • Design appropriate validation approaches for biomedical signal processing and machine learning experiments
  • Evaluate experimental results, technical assumptions, and analytical conclusions for scientific consistency
  • Produce clear technical documentation describing methods, computational workflows, performance assessments, and research findings
  • Apply lessons from scientific competitions and benchmark evaluations to improve task design and solution quality
  • Maintain rigorous standards for reproducibility, analytical accuracy, technical clarity, and timely completion of assigned deliverables

Ideal Profile

  • Participation in a PhysioNet/Computing in Cardiology Challenge or comparable biomedical signal processing or scientific machine learning competition
  • Strong practical expertise in biomedical signal processing, physiological data analysis, or computational biomedical research
  • Demonstrated personal technical contributions to scientific competitions, research projects, or advanced computational workflows
  • Strong competition results supported by identifiable technical contributions are particularly relevant
  • Publicly documented technical work through official competition results, conference papers, technical reports, open-source repositories, or meaningful code contributions is highly valued
  • Hands-on experience analysing physiological signals such as ECG, EEG, PPG, EMG, respiratory recordings, polysomnography, or comparable clinical time-series data
  • Strong programming skills in Python, MATLAB, or another scientific computing environment
  • Practical experience with signal filtering, segmentation, feature extraction, statistical analysis, and computational modelling
  • Familiarity with machine learning techniques for classification, forecasting, representation learning, or physiological signal interpretation
  • Experience developing reproducible experimental workflows and validating computational results
  • Ability to explain technical decisions, methodological assumptions, experimental findings, and individual research contributions clearly
  • Strong understanding of scientific evaluation methodologies, performance metrics, and model validation approaches
  • Excellent analytical reasoning, mathematical problem-solving, critical-thinking, and research capabilities
  • Exceptional attention to detail and commitment to accurate, well-documented technical work
  • Experience working with noisy physiological datasets, complex biomedical signals, or challenging real-world measurement conditions
  • Ability to work independently, manage technical assignments, and communicate scientific findings effectively
  • Familiarity with scientific benchmarking, computational biology, or interdisciplinary biomedical machine learning research is advantageous
  • Prior experience with AI training projects or model evaluation initiatives is not required

Engagement Details

  • Freelance / contract engagement
  • Fully remote — open worldwide
  • Compensation: $60–$120/hour advertised rate range
  • Payment Structure: Output-based compensation, with payment made per completed task that meets project specifications
  • Expected Commitment: Approximately 15 hours per week
  • Flexible Schedule: Professionals may choose their preferred working hours and days, including weekends
  • 50 available openings
  • Specialised technical consulting role spanning biomedical signal processing, physiological time-series analysis, machine learning, and AI evaluation
  • Work will involve scientific problem development, computational modelling, signal analysis, reference solution creation, and technical quality assessment
  • Task requirements and compensation will depend on the specifications of individual assignments
  • Selection may include screening questions, an approximately 30-minute AI interview, and a hiring manager review
  • Selected professionals should be prepared to begin initial assignments within 24–48 hours of completing onboarding
  • Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy

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