🧬 PhysioNet Competitor Jobs Worldwide | $60–$120/Hour Remote Openings 50

🧬 PhysioNet Competitor Jobs at micro1 – Remote Worldwide | $60–$120/Hour Are you a biomedical signal processing specialist, machine learning researcher, or scientific programmer with experience analyzing physiological data? micro1 is engaging experts for a remote PhysioNet Competitor (Biomedical Signal Processing & ML) opportunity. This contractor role focuses on biomedical signal processing, scientific programming, machine learning, and the development of challenging technical problems to help improve next-generation AI systems. Candidates with experience in the PhysioNet Challenge, Computing in Cardiology competitions, or comparable biomedical signal processing and scientific machine learning competitions may be particularly well suited to this opportunity. 📌 Job Details - 🏢 Company: micro1 - 💼 Job Title: PhysioNet Competitor (Biomedical Signal Processing & ML) - 🌍 Location: Global – Fully Remote - 💰 Advertised Pay: $60–$120 per hour - ⏰ Workload: Approximately 15 hours per week - 📄 Job Type: Contractor - 📢 Listed Openings: 50 - 🗓️ Schedule: Flexible, including weekends - 🧠 Required Skills: Biotechnology, Biology, and Machine Learning - 🔬 Specialization: Biomedical Signal Processing and Physiological Data Analysis 📝 Job Description micro1 is looking for specialists with practical experience in biomedical signal processing, physiological time-series analysis, and machine learning competitions. In this role, selected experts will use their scientific programming and analytical skills to create, solve, review, and evaluate advanced technical problems. Their work will support AI training projects that require accurate scientific reasoning and reproducible computational solutions. The work may involve analyzing noisy physiological signals, developing preprocessing pipelines, extracting meaningful features, training machine learning models, and evaluating experimental results. Candidates should be able to explain the technical work they personally completed, the methods they developed, the experiments they performed, and how they validated their findings. Previous experience working on AI training projects is not required. However, relevant biomedical signal processing, scientific programming, or competition experience is important. 💼 Key Responsibilities Selected experts may be responsible for the following tasks: - 🧬 Develop challenging technical problems involving biomedical signals and time-series data. - 📊 Analyze physiological data using signal processing and machine learning techniques. - 🧹 Preprocess noisy real-world biomedical signals. - 🎛️ Apply filtering, segmentation, and feature extraction methods. - 🤖 Develop or evaluate machine learning models. - 🐍 Create reference solutions using Python or other scientific computing tools. - 📈 Perform classification, forecasting, and representation-learning tasks. - 🧪 Design experiments and evaluate model performance. - ✅ Validate results and ensure that solutions are scientifically sound and reproducible. - 📝 Explain technical methods, assumptions, experimental choices, and findings. - 🔍 Review solutions for accuracy, reliability, and scientific rigor. 🎓 Preferred Qualifications Applicants should review the following qualifications before applying. 1. PhysioNet or Similar Competition Experience Experience participating in any of the following may be particularly valuable: - 🏆 PhysioNet Challenge - 🔬 Computing in Cardiology Challenge - 🧠 Biomedical signal processing competitions - 🤖 Scientific machine learning competitions - 📊 Comparable research or data science challenges involving physiological signals Strong competition results and evidence of meaningful personal technical contributions may strengthen an application. 2. Biomedical Signal Processing Experience Candidates should ideally have hands-on experience working with physiological signals, including: - ❤️ ECG (electrocardiography) - 🧠 EEG (electroencephalography) - 💓 PPG (photoplethysmography) - 💪 EMG (electromyography) - 🌬️ Respiratory signals - 😴 Polysomnography data - 🏥 Other clinical or physiological time-series data Experience working with real-world data, including noisy or incomplete signals, is especially relevant. 3. Scientific Programming and Machine Learning Suitable candidates should be comfortable with relevant scientific programming environments, such as: - 🐍 Python - MATLAB - Signal processing libraries and tools - Machine learning frameworks - Statistical analysis and experimental evaluation methods Applicants should be able to develop clear, reproducible reference solutions and explain how their methods work. 4. Evidence of Technical Contributions Public evidence of relevant work may include: - 📄 Conference papers or technical reports - 💻 Open-source repositories - 🏆 Official competition or team results - 🧪 Research publications - 🔧 Meaningful code contributions Candidates should be prepared to explain their own contributions rather than relying only on the overall achievements of a team. 🛠️ Areas of Work This opportunity may involve several technical areas. - Biomedical signal processing - Physiological time-series analysis - Signal filtering and preprocessing - Feature extraction and segmentation - Machine learning and classification - Forecasting and representation learning - Scientific computing and reproducible experiments - Biomedical data analysis and model validation The exact tasks may vary according to project requirements and the specialist's experience. 💰 Salary and Compensation The advertised compensation range is $60–$120 per hour. However, the payment model is output-based rather than a guaranteed fixed hourly salary. - 💵 Advertised rate: $60–$120/hour - ⏰ Expected workload: Approximately 15 hours per week - 📄 Payment structure: Payment for tasks meeting project specifications - 📋 Minimum submission requirements: Apply The time needed to complete a task may vary depending on its complexity, the expert's experience, and their workflow. Actual earnings depend on accepted submissions and the applicable project terms. 🌍 Work Location and Schedule This is a fully remote contractor opportunity advertised for candidates globally. - 🏠 Work remotely. - 🗓️ Choose your working hours and days. - ⏰ Work approximately 15 hours per week. - 🌙 Weekend work is optional. - 🚀 Be prepared to start soon after onboarding if selected. Applicants should confirm their country eligibility, contracting arrangements, and payment options during the recruitment process. 🧑‍💻 Application and Hiring Process The recruitment process includes three stages. Step 1: Apply Online Submit your application and complete the required screening questions. Step 2: Complete the AI Interview Complete an AI interview of approximately 30 minutes. Step 3: Hiring Manager Review Qualified applicants proceed to the hiring manager review for further evaluation. ⏳ Expected Start Timeline According to the listing, roles are typically filled within 48 hours. Selected candidates are expected to begin their first task within 24–48 hours after completing onboarding. These timelines are stated by the listing and do not guarantee selection or hiring. 👥 Who Should Apply? This opportunity may be suitable for: - Biomedical signal processing specialists - Machine learning researchers working with physiological data - Scientific programmers with biomedical data experience - Researchers who have participated in PhysioNet or similar competitions - Data scientists experienced in clinical time-series analysis - Professionals with demonstrated work in ECG, EEG, PPG, EMG, or related signals Candidates should carefully review the requirements and be prepared to demonstrate relevant technical experience and personal contributions. Apply Now