Current Vacancies

Postdoctoral Researcher/Assistant Professor (Temporal Medical Reasoning & Diagnosis)

博士后/助理研究员（时序医学推理与诊断方向）

Job Responsibilities

1. Build disease risk prediction models using multimodal temporal data (EMRs, EEG, ECG).

2. Design causal discovery systems integrating physiological signals (ECG/EEG) and clinical knowledge.

3. Develop guideline-compliant diagnostic reasoning frameworks.

4. Publish in top-tier journals/conferences and file patents.

5. Contribute to funding applications and real-world deployment.

Requirements

1. PhD in Biomedical Informatics, Computer Science, Applied Mathematics, or related fields.

2. Mastery of time-series analysis (RNN/Transformer/State-space Models) and medical causal inference methods.

3. Proficiency in PyTorch/TensorFlow and causal discovery tools (DoWhy, CausalNex).

4. Strong publication record in premier venues.

5. Healthcare research experience is advantageous.

Application Method

Please submit CV, representative publications (highlighting 1-2 first-author papers) to hr02@cair-cas.org.hk. The subject of the email should be marked as Application for [Postdoctoral Researcher/Assistant Professor (Temporal Medical Reasoning & Diagnosis)]-[Name].

岗位职责

1. 开发基于电子病历、脑电、心电图等多模态时序诊疗数据的疾病风险预测模型。

2. 设计融合生理信号（ECG/EEG）与临床知识的因果发现与动态决策系统。

3. 构建符合临床指南的可信诊断推理框架。

4. 发表高水平研究论文（包括CVPR、ICCV、NeurIPS、ICLR、TPAMI、TMI、MedIA 等），申请国家发明专利。

5. 协助科研项目申请工作。

6. 协助研究成果的落地转化工作。

职位要求

1. 博士学位，生物医学信息学、计算机科学、应用数学等相关领域。

2. 精通时间序列分析经典方法与模型（RNN/Transformer/State-space Models）。

3. 精通面向医学领域的经典因果发现与推断方法。

4. 熟练掌握C/C++、PyTorch/TensorFlow框架。

5. 具备较强的科研能力，有顶级会议或期刊论文发表经历。

6. 有医疗场景研究经验者优先。

申请方式

请将个人简历、代表性出版物（1-2篇第一作者论文）发送至hr02@cair-cas.org.hk。邮件主题请注明应聘[博士后/助理研究员（时序医学推理与诊断方向）]-[姓名]。