首都醫(yī)學科學創(chuàng)新中心[Chinese Institutes for Medical Research (CIMR), Beijing]（簡稱創(chuàng)新中心）是北京市新成立的具有獨立法人資格的新型研發(fā)機構。創(chuàng)新中心以推動醫(yī)學科學發(fā)展、改善人類健康為目標，開展生物醫(yī)學研究，致力于提升醫(yī)學科學創(chuàng)新與成果轉化能力，提高疾病診斷和治療水平。我們將匯聚世界高水平科學家，與首都醫(yī)學教育和科學研究優(yōu)質資源緊密合作，打造多學科交叉融合的科研平臺，綜合自由探索、醫(yī)學目標導向、有組織科研等途徑，實踐新型科研模式和體制機制，培養(yǎng)適應醫(yī)學科學創(chuàng)新發(fā)展的優(yōu)秀人才，逐步推進醫(yī)教研產的深度融合。

范昊教授，現(xiàn)任首都醫(yī)學科學創(chuàng)新中心分子與細胞治療研究所資深研究員。范教授本科畢業(yè)于中國科學技術大學，于荷蘭格羅寧根大學獲得博士學位，隨后在加州大學舊金山分校 (UCSF) 開展科研工作。在加入 CIMR 之前，曾任新加坡科技研究局 (A*STAR) 生物信息學研究所資深研究員。

范昊實驗室致力于通過 “AI + 物理驅動” 的雙引擎模式推動 分子治療 (Molecular Therapeutics) 創(chuàng)新。我們強調：

●利用 預測性與生成式 AI 方法 (Predictive & Generative AI) 加速小分子藥物與功能蛋白的研發(fā)；

●結合傳統(tǒng)的基于 物理和化學的藥物化學與計算機輔助藥物開發(fā)方法（如高精度分子動力學模擬、自由能微擾 FEP 等）提供精準驗證，兩者互相輔助。

實驗室網(wǎng)站：https://www.cimrbj.ac.cn/channel/2013492182471806976.html

實驗室研究方向與近期成果

1.AI驅動的藥物與酶設計：開發(fā)了機器學習方法探索半乳糖氧化酶底物活性 (ACS Catalysis 2024) 及新型氟化酶設計 (Chemical Science 2025)。

2.生成式 AI 與中藥現(xiàn)代化：開發(fā)了首個中藥化學空間優(yōu)化流程 “TCM-Navigator” (Briefings in Bioinformatics 2025)。

3.GPCR 與激酶機制：揭示了 GPR84 的選擇性分子基礎 (Nat Comm 2023) 及 BRAF 突變耐藥機制 (Science Advances 2021)。

4.精準配體開發(fā)：基于對比神經(jīng)網(wǎng)絡開發(fā)了通用蛋白靶點配體預測方法 (https://www.biorxiv.org/content/10.1101/2025.03.16.643501v2)。

1.AI 方法開發(fā)：構建應用于生化領域的創(chuàng)新模型架構。

2.藥物化學與計算機輔助藥物開發(fā)：從虛擬篩選到先導化合物優(yōu)化，利用自由能微擾等高精度方法開展研究。

3.蛋白質工程：功能蛋白、抗體及合成酶的發(fā)現(xiàn), 優(yōu)化，與從頭設計。

(1) 副研究員 / 助理研究員 (1-2名)

主要職責：

1.領導上述核心方向的課題研究，利用 AI 或高精度計算工具指導藥物/蛋白設計；

2.協(xié)助 PI 指導研究生及撰寫項目申請；兼任實驗室部分管理工作 (Part-time Lab Manager)。

任職要求：

1.具有藥物化學、計算化學、CS 或生信相關博士學位；副研需 3 年以上相關經(jīng)歷；

2.精通 MD/FEP 方法，并具備 CNN/GNN/PLM 等模型的開發(fā)與應用能力；

3.以第一作者身份發(fā)表過高水平論文，具備優(yōu)秀的英文寫作與團隊協(xié)作能力。

(2) 博士后 (1-2名)

主要職責：

在 PI 指導下獨立開展 AI 算法開發(fā)、高精度藥物設計或蛋白質工程課題。

任職要求：

1.已獲得或即將獲得博士學位（專業(yè)不限，歡迎跨學科背景）；

2.具備獨立解決復雜科學問題的能力，有主流期刊發(fā)表記錄。

3.注：不強制專業(yè)必須為純 AI 方向，只要在計算藥研或算法應用領域有扎實功底即可。

(3) AI / 算法工程師 (1-2名)

主要職責：

算法落地、大模型訓練及維護實驗室高性能計算資源 (GPU 集群)。

任職要求：

1.計算機、數(shù)學或軟件工程背景。不強制要求博士學位；

2.優(yōu)秀碩士且有 3 年以上高水平行業(yè)/研究經(jīng)驗者優(yōu)先，看重實際模型開發(fā)能力。

請將以下材料發(fā)送至 fanhao@cimrbj.ac.cn

1.個人簡歷；

2.研究興趣說明或未來研究計劃；

3.強烈建議提供：GitHub 鏈接、代表性代碼或研究案例；

4.2-3 名推薦人的姓名及聯(lián)系方式。

郵件主題：應聘者名字+具體應聘職位+高校人才網(wǎng)。本招聘長期有效，招滿為止。同時歡迎對計算生物學感興趣的各階段實習生、聯(lián)培生前來交流。【快捷投遞：點擊下方“立即投遞/投遞簡歷”，即刻進行職位報名】

The Chinese Institute of Medical Research (CIMR) is a newly established institution dedicated to fundamental and translational medical research in Beijing. The CIMR is committed to building a scientist-centered governance framework and fostering a diverse and inclusive work environment. For more information, please visit our website: www.cimrbj.ac.cn.

Dr. Fan is a Senior Investigator at the Institute of Molecular and Cellular Therapeutics, CIMR. He earned his B.S. from the University of Science and Technology of China (USTC) and his Ph.D. from the University of Groningen. He conducted his postdoctoral and research scientist work at the University of California, San Francisco (UCSF). Prior to joining CIMR, he served as a Senior Principal Investigator at the Bioinformatics Institute, A*STAR, Singapore.

The Fan Lab drives innovation in Molecular Therapeutics through a "Dual-Engine" approach:

●AI-Driven Discovery: Leveraging Predictive and Generative AI to accelerate the development of small-molecule drugs and functional proteins.

●Physics-Based Refinement: Utilizing high-precision computational chemistry and drug design (e.g., Free Energy Perturbation/FEP, Molecular Dynamics) to provide physical grounding and accurate validation.

These two pillars complement each other to bridge the gap between AI-generated designs and experimental reality.

Lab Website: https://www.cimrbj.ac.cn/en/channel/2013512987888979968.html

1.AI-Driven Enzyme Design: Developed machine learning workflows to explore the substrate scope of galactose oxidase (ACS Catalysis 2024) and engineered novel fluorinases (Chemical Science 2025).

2.Generative AI for Medicine: Created "TCM-Navigator," the first deep-learning-based end-to-end workflow for optimizing Traditional Chinese Medicine chemical spaces (Briefings in Bioinformatics 2025).

3.GPCR & Kinase Mechanisms: Elucidated the molecular basis of GPR84 selectivity (Nat Comm 2023) and the resistance mechanisms of BRAF mutations (Science Advances 2021).

4.Precision Ligand Discovery: Developed a contrastive neural network-based AI method for ligand prediction against general protein targets (https://www.biorxiv.org/content/10.1101/2025.03.16.643501v2).

1. Research Associate / Assistant Investigator (1–2 positions)

Main Responsibilities:

●Lead research projects in AI-aided drug discovery or protein/enzyme engineering.

●Assist the PI in supervising graduate students and drafting grant proposals.

●Part-time Lab Management: Oversee daily operations, computational resource allocation, and academic exchange activities.

Qualifications:

●Ph.D. in Computer Science, Bioinformatics, Computational Chemistry, Medicinal Chemistry, or a related field. Research Associates require 3+ years of postdoctoral or industry experience.

●Deep understanding of the synergy between AI and Physics-based models. Proficiency in FEP/MD or Generative Models/GNNs is highly preferred.

●A strong publication record as a first author and excellent English writing/leadership skills.

2. Postdoctoral Fellow (2–3 positions)

Main Responsibilities:

●Conduct independent research in AI algorithm development, high-precision drug design, or protein engineering under the PI’s guidance.

Qualifications:

●Ph.D. in a relevant field (Interdisciplinary backgrounds in drug chemistry, bioinformatics, or AI are welcome).

●Note: A specialized degree in AI is not mandatory; we value demonstrated proficiency in applying ML/DL to biological problems.

●Proven track record of original research in reputable journals.

3. AI / Algorithm Engineer (1–2 positions)

Main Responsibilities:

●Develop and deploy generative/predictive models and maintain high-performance GPU clusters.

Qualifications:

●Degree in Computer Science, Mathematics, or Software Engineering. A Ph.D. is not mandatory.

●Candidates with a Master’s degree and 3+ years of high-level industry/research experience are preferred. Proficiency in PyTorch/TensorFlow is essential.

Please send the following materials to fanhao@cimrbj.ac.cn:

1.An updated CV/Resume.

2.A statement of research interests or a future research plan.

3.Highly Recommended: Link to GitHub/code samples or a portfolio of research cases.

4.Contact details for 2–3 professional referees.

Email Subject: [Name] + [Specific Position Applied For]. Recruitment is open until positions are filled. We also welcome interns and joint trainees interested in computational biology.

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