3D云与野火羽流辐射模拟方向博士职位

PhD: 3D radiation in clouds and wildfire plumes using GPUs and machine learning

Wageningen University & Research · 荷兰 · Wageningen

原帖优先:申请材料、截止时间与资格以原帖和学校官方说明为准。

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研究内容
研究利用GPU和机器学习开发用于大气的三维蒙特卡洛射线追踪器,探究三维辐射对云、太阳能利用、气候敏感度及野火羽流的影响。
申请条件
原文未说明具体学位门槛,需负责物理方向的研究,并与莱顿大学计算机科学专业的博士生协同合作。
待遇
月薪为 EUR 3204 至 4051,可使用国家及欧洲超级计算机(Snellius, LUMI)和专用GPU硬件。
申请方式
截止日期为2026年11月1日,请通过官方渠道申请。
材料清单
  • 原文未说明

由 gemini-2.5-flash-lite 生成,博士岗判定置信度 60%。

结构化信息

截止
(Europe/Amsterdam) 剩 23 天
学科
地球与环境
合同类型
雇佣合同
原文薪资
EUR 3,204–4,051 / 月(税前)
税后月薪(估)
¥19,000–¥24,000;房租后 ¥11,100–¥16,100
估算假设
单身、无子女、雇佣合同的粗略估算,以学校 offer 为准;扣除率 25%;汇率日期 2026-10-01
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导师
Chiel van Heerwaarden
来源
AcademicTransfer(荷兰学术招聘) · 最近核对 2026-10-09
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原文

This PhD project is part of ORCRIST: Optimized Ray-tracing for Cloud-Radiation Interaction Simulations in 3D using GPUs and Machine Learning, funded by the NWO Open Technology Programme.

Sunlight and heat radiation move through the atmosphere in three dimensions, yet almost every weather and climate model still treats radiation as a purely vertical process. We want to change that. As a PhD candidate in Wageningen, you will help build the first Monte Carlo ray tracer for the atmosphere that is fast enough for forecasting, and use it to find out how 3D radiation shapes clouds, solar energy availability, climate sensitivity and wildfire plumes. You will work in a tandem with a fellow PhD student in computer science at Leiden University, who focuses on GPU optimization, while you lead the physics. Together you will develop a machine-learning denoiser that brings the ray tracer to operational speed. Your research has a clear route to society: throughout the project you will work with a user committee of KNMI, weather forecasting company Whiffle, TNO, software company VORtech and Columbia University.

Your duties and responsibilities include: • Setting up three benchmark simulations in the GPU-accelerated model MicroHH: solar energy forecasting, tropical climate, and a real wildfire plume. • Developing a 3D thermal radiation solver within the RTE-RRTMGP-CPP ray tracer, including smart strategies for sampling radiation sources. • Quantifying how 3D radiation changes clouds, surface irradiance and plume dynamics compared to conventional 1D radiation. • Co-developing a physics-informed machine-learning denoiser with your Leiden colleague, and evaluating it together with the user committee in operational settings. • Publishing your results in leading journals and presenting them at international conferences and to users from the field.

You will work here The research is embedded within the chair of Meteorology ( Meteorology and Air Quality Group ), led by Prof. Vilà-Guerau de Arellano. You will be supervised by Dr.ir. Chiel van Heerwaarden and Dr.ir. Menno Veerman, both at WUR, and by Dr. Ben van Werkhoven at Leiden University. The team is small, open and collaborative, and has a track record of building widely used open-source simulation codes. You will have access to national and European supercomputers (Snellius, LUMI) and dedicated GPU hardware.

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