PhysioNet Competitor (Biomedical Signal Processing & ML)
职位标题：FysioNet 竞争对手 （生物医学信号处理和 ML）薪水：60美元~120美元/小时 地点：全球，完全远程工作类型：承包商 （每周15个小时）时间表：灵活 - - 你选择你工作的时间和天气，包括周末如果想要的微1正在吸引有 PhysioNet 挑战或类似的生物医学信号加工和机器学习比赛经验的专家。在这个角色中，你将应用你的科学编程，信号处理和建模专业知识来创建，解决和评估帮助改善下一代人工智能系统的先进技术问题。我们正在寻找那些直接与生理信号工作的人，能够将噪音的现实数据转化为严格可复制分析。强有力的候选人将能够解释他们开发的方法，他们所运行的实验，自己拥有的技术决策以及如何验证他们的结果。不需要在人工智能培训项目上之前的工作经验。工作范围 ·创建涉及生物医学信号，时间系列数据，信号处理和机器学习的技术严格，自主问题。·使用 Python, MATLAB 或其他适当科学计算环境制定明确参考解决方案。· 参与一个 PhysioNet/Computing in Cardiology Challenge 或类似的生物医学信号处理或科学机器学习竞赛。·强大的比赛结果和个人实质性技术贡献的证据。· 公开证明你的工作通过官方团队成果，会议文件，技术报告，开源存储库或有意义的贡献代码。· 实践经验，使用电脑电图，EEG,PPG,EMG,呼吸信号，聚声谱或其他临床时间系列数据。过程·应对角色并完成选问题。· 完成约30分钟的 AI 面试。· 完整招聘管理员审查。补偿结构补偿是基于输出。专家每项工作按项目规格付费。每个任务所需的时间可能根据专家的经验和工作流程而异。申请要求最低提交。启动时间表 &可用性 · 我们通常填写职位
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Role Title: 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
micro1 is 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.
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.
Originally posted on Himalayas