首都醫(yī)學(xué)科學(xué)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(dú)立法人資格的新型研發(fā)機(jī)構(gòu)。創(chuàng)新中心以推動醫(yī)學(xué)科學(xué)發(fā)展、改善人類健康為目標(biāo)，開展生物醫(yī)學(xué)研究，致力于提升醫(yī)學(xué)科學(xué)創(chuàng)新與成果轉(zhuǎn)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(xué)家，與首都醫(yī)學(xué)教育和科學(xué)研究優(yōu)質(zhì)資源緊密合作，打造多學(xué)科交叉融合的科研平臺，綜合自由探索、醫(yī)學(xué)目標(biāo)導(dǎo)向、有組織科研等途徑，實(shí)踐新型科研模式和體制機(jī)制，培養(yǎng)適應(yīng)醫(yī)學(xué)科學(xué)創(chuàng)新發(fā)展的優(yōu)秀人才，逐步推進(jìn)醫(yī)教研產(chǎn)的深度融合，助力首都醫(yī)科大學(xué)成為世界一流的醫(yī)科大學(xué)。
一、 實(shí)驗(yàn)室介紹
范昊教授，現(xiàn)任首都醫(yī)學(xué)科學(xué)創(chuàng)新中心分子與細(xì)胞治療研究所資深研究員。范教授本科畢業(yè)于中國科學(xué)技術(shù)大學(xué)，于荷蘭格羅寧根大學(xué)獲得博士學(xué)位，隨后在加州大學(xué)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(xué)研究所資深研究員。
范昊實(shí)驗(yàn)室致力于通過“AI + 物理驅(qū)動”的雙引擎模式推動分子治療（Molecular Therapeutics）創(chuàng)新。我們強(qiáng)調(diào)：
利用預(yù)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(jié)合傳統(tǒng)的基于物理和化學(xué)的藥物化學(xué)與計(jì)算機(jī)輔助藥物開發(fā)方法（如高精度分子動力學(xué)模擬、自由能微擾FEP等）提供精準(zhǔn)驗(yàn)證，兩者互相輔助。
實(shí)驗(yàn)室研究方向與近期成果
1. AI驅(qū)動的藥物與酶設(shè)計(jì)：開發(fā)了機(jī)器學(xué)習(xí)方法探索半乳糖氧化酶底物活性（ACS Catalysis 2024）及新型氟化酶設(shè)計(jì)（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(xué)空間優(yōu)化流程“TCM-Navigator”（Briefings in Bioinformatics 2025）。
3. GPCR與激酶機(jī)制：揭示了GPR84的選擇性分子基礎(chǔ)（Nat Comm 2023）及BRAF突變耐藥機(jī)制（Science Advances 2021）。
4. 精準(zhǔn)配體開發(fā)：基于對比神經(jīng)網(wǎng)絡(luò)開發(fā)了通用蛋白靶點(diǎn)配體預(yù)測方法（https://www.biorxiv.org/content/10.1101/2025.03.16.643501v2）。
二、 招聘方向
1. AI方法開發(fā)：構(gòu)建應(yīng)用于生化領(lǐng)域的創(chuàng)新模型架構(gòu)。
2. 藥物化學(xué)與計(jì)算機(jī)輔助藥物開發(fā)：從虛擬篩選到先導(dǎo)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(zhì)工程：功能蛋白、抗體及合成酶的發(fā)現(xiàn)、 優(yōu)化與從頭設(shè)計(jì)。
掃碼了解更多（請點(diǎn)擊鏈接查看詳情）
三、 副研究員/助理研究員（1-2名）
(一) 主要職責(zé)
1. 領(lǐng)導(dǎo)上述核心方向的課題研究，利用AI或高精度計(jì)算工具指導(dǎo)藥物/蛋白設(shè)計(jì)；
2. 協(xié)助PI指導(dǎo)研究生及撰寫項(xiàng)目申請；兼任實(shí)驗(yàn)室部分管理工作（Part-time Lab Manager）。
(二) 任職要求
1. 具有藥物化學(xué)、計(jì)算化學(xué)、CS或生信相關(guān)博士學(xué)位；副研需3年以上相關(guān)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(yīng)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(tuán)隊(duì)協(xié)作能力。
四、 博士后（1-2名）
(一) 主要職責(zé)
1. 在PI指導(dǎo)下獨(dú)立開展AI算法開發(fā)、高精度藥物設(shè)計(jì)或蛋白質(zhì)工程課題。
(二) 任職要求
1. 已獲得或即將獲得博士學(xué)位（專業(yè)不限，歡迎跨學(xué)科背景）；
2. 具備獨(dú)立解決復(fù)雜科學(xué)問題的能力，有主流期刊發(fā)表記錄。
注：不強(qiáng)制專業(yè)必須為純AI方向，只要在計(jì)算藥研或算法應(yīng)用領(lǐng)域有扎實(shí)功底即可。
五、 AI / 算法工程師（1-2名）
(一) 主要職責(zé)
1. 算法落地、大模型訓(xùn)練及維護(hù)實(shí)驗(yàn)室高性能計(jì)算資源（GPU集群）。
(二) 任職要求
1. 計(jì)算機(jī)、數(shù)學(xué)或軟件工程背景，不強(qiáng)制要求博士學(xué)位；
2. 優(yōu)秀碩士且有3年以上高水平行業(yè)/研究經(jīng)驗(yàn)者優(yōu)先，看重實(shí)際模型開發(fā)能力。
六、 福利待遇
1. 根據(jù)應(yīng)聘者的工作經(jīng)驗(yàn)和能力，提供具有市場競爭力的薪酬（詳情面談）；
2. 享受五險(xiǎn)一金、帶薪年假、體檢和補(bǔ)充醫(yī)療保險(xiǎn)等；
3. 支持個人職業(yè)發(fā)展，提供必要的工作相關(guān)專業(yè)培訓(xùn)。
七、 申請方法
請將以下材料發(fā)送至fanhao@cimrbj.ac.cn，請【點(diǎn)擊下方“立即投遞/投遞簡歷”，即刻進(jìn)行職位報(bào)名】
1. 個人簡歷；
2. 研究興趣說明或未來研究計(jì)劃；
3. 強(qiáng)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(yīng)聘者名字 + 具體應(yīng)聘職位。
聯(lián)系人：范昊
聯(lián)系地址：北京市豐臺區(qū)右安門外西頭條10號-首都醫(yī)學(xué)科學(xué)創(chuàng)新中心
本招聘長期有效，至招聘到合適人選為止。
ABOUT CIMR
Chinese Institutes for Medical Research (CIMR) is a newly founded institution dedicated to fundamental and translational medical research. CIMR is located at the main campus of the Capital Medical University, Beijing, China and 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
Laboratory Introduction
Dr. Fan is an Investigator at Chinese Institute for 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.
Recent Research Highlights
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).
Scan to Learn More（請點(diǎn)擊鏈接查看詳情）
Open Positions
Research Associate / Assistant Investigator (1–2 positions)
Main Responsibilities
a. Lead research projects in AI-aided drug discovery or protein/enzyme engineering.
b. Assist the PI in supervising graduate students and drafting grant proposals.
c. Part-time Lab Management: Oversee daily operations, computational resource allocation, and academic exchange activities.
Qualifications
a. Ph.D. in Computer Science, Bioinformatics, Computational Chemistry, Medicinal Chemistry, or a related field. Research Associates require 3+ years of postdoctoral or industry experience.
b. Deep understanding of the synergy between AI and Physics-based models. Proficiency in FEP/MD or Generative Models/GNNs is highly preferred.
c. A strong publication record as a first author and excellent English writing/leadership skills.
Postdoctoral Fellow (1–2 positions)
Main Responsibilities
a. Conduct independent research in AI algorithm development, high-precision drug design, or protein engineering under the PI’s guidance.
Qualifications
a. Ph.D. in a relevant field (Interdisciplinary backgrounds in drug chemistry, bioinformatics, or AI are welcome).
b. Note: A specialized degree in AI is not mandatory; we value demonstrated proficiency in applying ML/DL to biological problems.
c. Proven track record of original research in reputable journals.
AI / Algorithm Engineer (1–2 positions)
Main Responsibilities
a. Develop and deploy generative/predictive models and maintain high-performance GPU clusters.
Qualifications
a. Degree in Computer Science, Mathematics, or Software Engineering. A Ph.D. is not mandatory.
b. Candidates with a Master’s degree and 3+ years of high-level industry/research experience are preferred. Proficiency in PyTorch/TensorFlow is essential.
Welfare Treatment
a. Competitive salary based on the applicant’s work experience and ability (salary negotiable).
b. Social insurance and housing fund, supplementary medical insurance, physical examination and paid annual leave.
c. Opportunities for career development and available professional guidance.
How to apply
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].
Contact person: Hao Fan
This recruitment is valid for the long term until a suitable candidate is recruited.
信息來源于網(wǎng)絡(luò)，如有變更請以原發(fā)布者為準(zhǔn)。
來源鏈接：
https://mp.weixin.qq.com/s/0X0L7HQzO-PJhF4Sp5B45w
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