香港大學(xué)化學(xué)系陳冠華教授課題組招收博士研究生
香港大學(xué)化學(xué)系理論化學(xué)講席教授陳冠華課題組現(xiàn)招收2至3名博士研究生。錄取學(xué)生將參與香港大學(xué)—加州理工學(xué)院聯(lián)合研究項目，研究方向為基于多尺度建模與機器學(xué)習(xí)的下一代高性能固態(tài)電解質(zhì)設(shè)計。該項目結(jié)合物理驅(qū)動的建模方法、先進(jìn)的機器學(xué)習(xí)算法以及實驗數(shù)據(jù)，深入探究鋰離子在聚合物基復(fù)合電解質(zhì)中的傳輸機制，并探索優(yōu)化策略，致力于開發(fā)具備高導(dǎo)電性與穩(wěn)定性的新型材料，為下一代鋰離子電池提供可靠的解決方案。
課題組配備豐富的科研資源，擁有30余張高性能GPU（如A100、A800等）及近30個高性能CPU計算節(jié)點，可充分滿足博士生在多尺度建模與機器學(xué)習(xí)等方向上的計算需求，積極支持學(xué)生開展創(chuàng)新性科研工作。所有錄取博士生均可獲得獎學(xué)金資助，目前資助金額為每月18,760港幣。誠邀在相關(guān)領(lǐng)域具有良好學(xué)術(shù)背景、并對材料模擬與機器學(xué)習(xí)研究充滿熱情的優(yōu)秀學(xué)生加入本課題組，共同開展前沿科學(xué)探索。
開發(fā)和應(yīng)用多尺度建模方法，研究鋰離子在聚合物基復(fù)合電解質(zhì)中的溶劑化和傳輸機制。構(gòu)建基于物理機制的代理函數(shù)以快速預(yù)測離子傳輸性能，并結(jié)合機器學(xué)習(xí)優(yōu)化固態(tài)電解質(zhì)的設(shè)計。
● 使用分子動力學(xué)模擬（MD）和量子化學(xué)計算（QC）研究鋰離子在聚合物基電解質(zhì)中的溶劑化結(jié)構(gòu)及動力學(xué)行為；
● 構(gòu)建粗粒化模型及基于物理機制的代理函數(shù)，加速離子傳輸性能的預(yù)測；
● 開發(fā)機器學(xué)習(xí)模型，提取潛在特征并優(yōu)化電解質(zhì)材料；
● 與高通量實驗生成的數(shù)據(jù)結(jié)合，驗證模擬結(jié)果并指導(dǎo)實驗設(shè)計。
專業(yè)背景：具有化學(xué)、材料科學(xué)、物理、計算化學(xué)、計算材料科學(xué)或相關(guān)領(lǐng)域的學(xué)士或碩士學(xué)位。
技術(shù)能力：
● 有高分子物理/化學(xué)知識者優(yōu)先；
● 熟悉分子動力學(xué)模擬工具（如LAMMPS、GROMACS）或量子化學(xué)計算軟件（如Gaussian、VASP）；
● 熟練掌握至少一種編程語言（如Python、C++或Fortran）；
● 有機器學(xué)習(xí)模型開發(fā)經(jīng)驗（如JAX、PyTorch）者優(yōu)先。
● 科研素質(zhì)：對固態(tài)電解質(zhì)材料研究具有濃厚興趣，具備獨立科研能力和團隊合作精神；具備良好的英語讀寫和溝通能力。
招生單位：香港大學(xué)化學(xué)系
申請條件：需滿足香港大學(xué)博士研究生入學(xué)要求（如雅思成績、GPA等）。
申請材料：個人簡歷、成績單、研究計劃、推薦信（2封及以上）。
截止日期：歡迎盡早申請，招生名額有限，錄滿為止。
有意申請者請將申請材料發(fā)送至胡老師郵箱ziyang1@hku.hk，郵件標(biāo)題請注明“PhD Application of [SURNAME], [Given Name]”，如“PhD Application of SHEN, Qing”。
PhD Opportunities in Theoretical Chemistry – Prof GuanHua Chen’s Research Group, Department of Chemistry, The University of Hong Kong
Professor GuanHua Chen, Chair Professor of Theoretical Chemistry in the Department of Chemistry at The University of Hong Kong (HKU), is currently seeking to recruit 2 to 3 PhD students. Successful candidates will participate in a joint research project between HKU and the California Institute of Technology (Caltech). The project focuses on the design of next-generation high-performance solid-state electrolytes, using a combination of multi-scale modelling and machine learning. By integrating physics-driven modelling, advanced machine learning algorithms, and experimental data, the project aims to uncover the ion transport mechanisms of lithium ions in polymer-based composite electrolytes and to develop optimisation strategies for new materials with high ionic conductivity and stability, ultimately contributing to the advancement of next-generation lithium-ion batteries.
The group is equipped with extensive computational resources, including over 30 high-performance GPU cards (such as A100 and A800) and nearly 30 high-performance CPU nodes. These resources fully support the computational needs of research in multi-scale modelling and machine learning, fostering an environment conducive to innovative doctoral research. All admitted PhD students will receive full scholarship support, currently set at HKD 18,760 per month. Talented and motivated candidates with relevant academic backgrounds and a strong interest in materials simulation and machine learning are warmly encouraged to apply.
To develop and apply multi-scale modelling approaches to investigate the solvation and transport mechanisms of lithium ions in polymer-based composite electrolytes. The project further aims to construct physics-informed surrogate models for rapid prediction of ion transport performance and to incorporate machine learning methods for the design and optimisation of solid-state electrolytes.
● Employ molecular dynamics (MD) simulations and quantum chemistry (QC) calculations to study solvation structures and dynamical behaviours of lithium ions in polymer electrolytes;
● Develop coarse-grained models and physics-based surrogate functions to accelerate the prediction of ionic transport properties;
● Construct and train machine learning models to identify key material features and optimise electrolyte composition;
● Integrate high-throughput experimental data to validate simulation results and guide experimental design.
Eligibility and Requirements
Background: Applicants should hold a Bachelor’s or Master’s degree in Chemistry, Materials Science, Physics, Computational Chemistry, Computational Materials Science, or a related field.
Skills:
● Prior knowledge in polymer chemistry/physics is preferred;
● Familiarity with molecular dynamics software (e.g., LAMMPS, GROMACS) or quantum chemistry packages (e.g., Gaussian, VASP);
● Proficiency in at least one programming language (e.g., Python, C++, or Fortran);
● Experience in machine learning model development (e.g., JAX, PyTorch) is a plus.
Research Competence:
A strong interest in solid-state electrolyte research; ability to conduct independent research; collaborative mindset; and solid command of written and spoken English.
Host Department: Department of Chemistry, The University of Hong Kong
Entry Requirements: Applicants must meet the PhD admission criteria of HKU, including English language proficiency (e.g., IELTS) and academic performance (e.g., GPA).
Application Materials: CV, academic transcripts, research proposal, and at least two letters of recommendation.
Deadline: Applications are reviewed on a rolling basis. Early submission is strongly encouraged as places are limited and offers will be made until the positions are filled.
Interested applicants should send their application materials to Dr Hu: ziyang1@hku.hk.
Email subject: “PhD Application of [SURNAME], [Given Name]”, e.g., “PhD Application of SMITH, John”.
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