Nuclear Physics Specialist - Fully Remote | Upto $100/hr
关于工作的职位，默科尔将精英创意和技术人才与领先的人工智能研究实验室联系起来。总部位于旧金山，我们的投资者包括 Benchmark,General Catalyst,Peter Thiel,Adam D'Angelo,Larry Summers 和 Jack Dorsey.职位：计算粒子 &核物理专家类型：合同补偿：$70$100/小时 地点：远程承诺：1520小时 /周角色责任 ·设计人工智能以真正的科学软件来解决研究生级计算机问题。·开发需要战略规划和多步的工作流程的问题在颗粒 &核物理学。·测试并对最先进的模型进行改进，以实现目标难度。· 使用 scikit-hep 和其他 HEP Python 工具进行数据分析和模拟。·独立工作以根据反来完善问题设计。·与团队协作，提高 AI 模型性能。资格必须有。·在相关的 STEM 领域 （MS,博士或相等研究经验）的毕业生级培训。·通过研究出版物或专业工作证明了科学软件库的熟练能力。· 强大的 Python 技能编写问题设置和解决方案验证器。· 在 Linux/终端环境中使用远程计算沙盒的工作的能力。·可供应15~20小时/周。最喜欢的：· 经验跨多个领域或工具。· 对基准或评估设计的熟悉。· 科学教学或考试/问题组设计的背景。·计算可复制性和容器化环境的经验。• 申请过程 （需要20~30分钟才能完成）· 上传简历 · AI 面试根据你的简历 · 提交表格资源 & 支持 · 关于面试进程和平台信息，请查看：· 任何帮助或支持，联系：PS:我们的团队每天审查应用程序。请完成你的 AI 面试和申请步骤以考虑这个机会。原始地发布在喜马拉雅山脉。
查看英文原文
About the job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position: Computational Particle & Nuclear Physics Expert
Type:Contract
Compensation:$70–$100/hour
Location:Remote
Commitment:15–20 hours/week
Role Responsibilities
- Design graduate-level computational problems for AI to solve using real scientific software.
- Develop problems requiring strategic planning and multi-step workflows in Particle & Nuclear Physics.
- Test and refine problems against state-of-the-art AI models to achieve target difficulty.
- Utilize scikit-hep and other HEP Python tools for data analysis and simulations.
- Work independently to refine problem designs based on feedback.
- Collaborate asynchronously with teams to improve AI model performance.
Qualifications
Must-Have
- Graduate-level training in a relevant STEM field (MS, PhD, or equivalent research experience).
- Proven proficiency with scientific software libraries, demonstrated through research publications or professional work.
- Strong Python skills for writing problem setups and solution validators.
- Ability to work in a Linux/terminal environment with remote compute sandboxes.
- Available for 15–20 hours/week.
Preferred
- Experience across multiple domains or tools.
- Familiarity with benchmark or evaluation design.
- Background in scientific teaching or exam/problem-set design.
- Experience with computational reproducibility and containerized environments.
Application Process (Takes 20–30 mins to complete)
- Upload resume
- AI interview based on your resume
- Submit form
Resources & Support
- For details about the interview process and platform information, please check:
- For any help or support, reach out to:
PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
Originally posted on Himalayas