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Open nowPosted 37 days ago

AI算法研究员-气象大模型方向

c0fu1j3a9m0/referral33 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen nowAI算法研究员-气象大模型方向c0fu1j3a9m0/referral · Shanghai, Chinese Mainland

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The clock on this job

Early applications get read.

12.1% of postings close within 7 days. Measured by our own scanner across the market. c0fu1j3a9m0/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.1%7 days
  4. 19.1%14 days
  5. 35.6%30 days
  6. 52.7%60 days
This job: posted 37 days ago

c0fu1j3a9m0/referral median: 1 days open

The posting

研究方向包括但不限于： 1. 视觉生成模型与气象预测融合 - 研究 DiT / Video DiT / Diffusion Transformer 等先进视频生成与预测架构在气象短临及中期预报中的应用； - 探索将气象演变视为高维时空视频生成过程，构建高分辨率、长时效的气象时空预测模型； - 研究基于条件生成（Conditioned Generation）的极端天气生成、气象降尺度（Downscaling）等任务。 2. 气象多模态基础模型 - 面向气象科学场景，研究 ERA5 再分析数据、卫星遥感影像、雷达观测、地面站点等多源气象模态的统一表示与建模； - 探索气象数据的 3D/4D Tokenizer、多模态时空对齐等关键技术； - 构建面向全球/区域天气预报、灾害预警的气象模型。 3. 物理一致性与后训练优化 - 研究面向气象模型的物理规律约束与对齐方法，提升模型在动力学、热力学等物理守恒定律上的表现； - 构建气象任务的自动评价器（如针对极端天气的 Reward Model），探索偏好学习在气象生成质量优化中的应用； - 提升模型在长时效预测中的稳定性，缓解误差累积与模糊化问题（Blurry predictions）。 4. 数据、评测与系统化落地 - 构建高质量、大规模的气象多模态预训练数据集与微调数据； - 建立气象大模型的 Benchmark、气象学指标自动评测体系； - 推动模型在实际业务场景中的应用验证。

岗位职责 1. 独立或协同开展气象大模型相关方向的前沿研究； 2. 负责模型架构设计、训练方案制定、实验实现、结果分析与论文撰写； 3. 构建高质量气象数据集、分布式训练流水线、评测体系和 Demo 系统； 4. 跟踪国际前沿进展，形成系统性技术判断，并推动关键方向落地； 5. 指导研究实习生或工程同学完成相关实验与系统开发； 6. 在 CVPR、ICCV、ECCV、NeurIPS、ICML、ICLR 等 AI 顶会，或 Nature、Science、BAMS、GRL 等交叉学科/气象类顶刊发表研究成果。

1. 获得计算机科学、人工智能、电子信息、自动化、应用数学、物理、大气科学、气象学等相关方向博士学位（特别优秀的硕士亦可）； 2. 具备扎实的深度学习、计算机视觉（CV）和机器学习基础； 3. 熟悉至少一个相关方向： - 图像/视频生成模型（Diffusion / DiT / Flow Matching）； - 气象大模型或地球系统建模（Earth System Modeling）； - 3D 视觉理解； 4 具有较强的代码实现能力，熟悉 PyTorch，有 DeepSpeed、Megatron、分布式训练等大规模集群训练经验者优先； 5. 具有独立科研能力，能够从问题定义、方法设计、实验验证到论文写作完整推进研究； 6. 具有良好的英文论文阅读与写作能力； 7. 具备团队协作能力、工程意识和长期研究投入意愿。

优先条件 具备以下经历者优先： 1. 在 CVPR、ICCV、ECCV、NeurIPS、ICML、ICLR 等顶级会议发表过相关论文； 2. 有大规模视频生成模型、时空预测模型训练经验； 3. 有 DiT、Video DiT、Flow Matching 等实际研究或工程经验； 4. 有 AI for Earth、气象大模型研发经验，熟悉气象数据格式（NetCDF, GRIB等）； 5. 有高质量开源项目、气象 Benchmark、数据集或系统建设经验； 6. 有多卡/多节点训练、算子优化、显存优化和推理部署经验。

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