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Open nowPosted 32 days ago

【校招】医疗世界模型算法青年研究员（因果分析方向）-医学智能中心

aicarrier/522799209 open roles

Where
Shanghai, Chinese Mainland
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Your applicationOpen now【校招】医疗世界模型算法青年研究员（因果分析方向）-医学智能中心aicarrier/522799 · Shanghai, Chinese Mainland

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This job: posted 32 days ago

The posting

医疗世界模型旨在学习患者健康状态的动态演化规律，将临床轨迹建模为可预测、可干预、可反事实推演的 "世界"，本岗位聚焦因果方向。 1.治疗效应估计与因果推断：研究在电子病历数据上的因果效应估计方法，包括个体 / 条件治疗效果估计、平均处理效应，以及时序场景下的因果推断（时变混杂、g-computation、边际结构模型等），区分治疗的真实效果与观察数据中的虚假相关。 2.反事实推理与因果世界模型：研究基于生成模型的反事实轨迹生成（条件扩散模型、流匹配模型），使生成模型具备治疗因果语义 —— 支持 "若未干预 / 若改变治疗方案，患者状态将如何演进" 的反事实推演，并研究反事实一致性、干预可交换性等因果校验方法。 3.因果导向的序列决策：将因果效应估计融入序列决策与强化学习框架，研究治疗方案对长期结局的因果影响评估与策略优化，支撑可解释、可推演的临床决策。 4.算法实现与实验评估：负责因果算法的实现、模型训练、实验设计与效果评估，持续优化方法。 5.问题抽象与成果产出：将临床问题抽象为可实现的因果问题，完成问题定义→实验设计→结果分析→结论沉淀；跟踪前沿论文，参与论文撰写、开源项目与技术成果产出。

1.计算机、人工智能、数学、统计学、自动化、生物医学工程等相关专业，博士及以上学历；具备相关项目经验者优先。 2.熟悉因果推断相关理论或生成式模型技术，两者兼备者优先。 3.熟悉医疗相关数据集（如 MIMIC, eICU 等），理解其数据结构、缺失机制与观察性偏倚来源。 4.具备较强的学习能力和科研兴趣，能够快速阅读论文、理解方法并进行复现和改进。

加分项 1.具有较好的数学基础，能够理解机器学习、因果推断、扩散模型中的基本公式与目标函数。 2.有时序因果推断经验（如 g-computation、边际结构模型、时变混杂处理）或反事实生成项目经验。 3.对强化学习有所了解，有将世界模型用作环境模拟器进行策略学习的经验。 4.具有较强的问题抽象与逻辑表达能力，能够将一个复杂临床问题清晰、准确地表达和解释。 5.有相关科研项目、论文复现、开源项目或论文发表经历。

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