远程表面建模专家 – 最高可达84元/小时
Surface Modeling Expert - Fully Remote | Upto $84/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：原子化和表面建模专家 （计算材料和催化）类型：合同补偿：$84/小时 地点：远程承诺：1040小时/周角色责任 ·贡献领域知识在第一原则和分子仿真。重点电子结构，表面和接口建模，吸收和反应能量构建高质量的培训和评估数据。· 审查和评估人工智能生成的科学推理。捕获错误并提高技术准确性。· 在原子化和表面建模方面设计和解决具有挑战性的问题。· 与定义的科学标准相比，定位模型输出量和排名率。提供明确的书面推理。· 将技术知识结构成有组织的，模型准备的数据。包括仿真设置，方法和结果。·在定义的时间内提供可靠，高质量的工作。资格必须拥有 · 使用第一原则或分子方法 （DFT,ab initio 分子动态学，经典 MD 或蒙特卡洛）的原子化建模实验。· 经验模拟表面，接口以及吸收或反应现象 （片模型，表面重建，过渡状态，NEB,微动力学）. · 经历模拟半导体相关材料或计算 （异质）加泰利斯的背景。·熟练使用标准工具 （例如 VASP,量子 ESPRESSO,CP2K,GPAW,LAMMPS,ASE,pymatgen）. · 在材料科学，化学，物理，化工工程或相应领域获得博士学位，理想情况下在博士课程以外拥有多年的研究经验。· 明确写作英语和简洁解释技术推理的能力。申请过程 （需要20~30分钟才能完成）· 下载恢复 ·基于简历的 AI 面试 · 提交表格资源和支持 · 对面试过程和平台信息详细了解：· 任何帮助或支持，请联系：PS:我们的团队每天审查申请。请完成你的 AI 面谈和申请步骤以考虑这个机会。最初发布在喜马拉雅山
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: Atomistic & Surface Modeling Experts (Computational Materials & Catalysis)
Type:Contract
Compensation:$84/hour
Location:Remote
Commitment:10–40 hours/week
Role Responsibilities
- Contribute domain expertise in first-principles and molecular simulation. Focus on electronic structure, surface and interface modeling, adsorption, and reaction energetics to build high-quality training and evaluation data.
- Review and evaluate AI-generated scientific reasoning. Catch errors and improve technical accuracy.
- Design and solve challenging problems in atomistic and surface modeling.
- Rate and rank model outputs against defined scientific criteria. Provide clear written reasoning.
- Structure technical knowledge into well-organized, model-ready data. Include simulation setups, methods, and results.
- Deliver reliable, high-quality work within defined timelines.
Qualifications
Must-Have
- Hands-on experience with atomistic modeling using first-principles or molecular methods (DFT, ab initio molecular dynamics, classical MD, or Monte Carlo).
- Experience modeling surfaces, interfaces, and adsorption or reaction phenomena (slab models, surface reconstructions, transition states, NEB, microkinetics).
- Experience modeling semiconductor-relevant materials or a background in computational (heterogeneous) catalysis.
- Proficiency with standard tooling (e.g., VASP, Quantum ESPRESSO, CP2K, GPAW, LAMMPS, ASE, pymatgen).
- A PhD in materials science, chemistry, physics, chemical engineering, or a related field, ideally with several years of research experience beyond the PhD.
- Clear written English and the ability to explain technical reasoning concisely.
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