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香港大学化学系理论化学讲席教授陈冠华课题组现招收2至3名博士研(yan)究(jiu)生。录取学生将(jiang)参与香(xiang)港大学—加(jia)州理工学院(yuan)联(lian)合(he)研(yan)究(jiu)项(xiang)目，研(yan)究(jiu)方(fang)向为基(ji)(ji)于多尺(chi)度(du)建(jian)模与机器学习的(de)(de)下一代(dai)高(gao)(gao)性能固态电解质设计。该项(xiang)目结(jie)合(he)物理驱动的(de)(de)建(jian)模方(fang)法(fa)、先进的(de)(de)机器学习算法(fa)以及实验数据，深入(ru)探究(jiu)锂离子在聚合(he)物基(ji)(ji)复合(he)电解质中的(de)(de)传(chuan)输机制，并探索优化策(ce)略，致力(li)于开(kai)发具备高(gao)(gao)导电性与稳定性的(de)(de)新型材料，为下一代(dai)锂离子电池(chi)提供(gong)可靠的(de)(de)解决方(fang)案。

课(ke)题组配备丰(feng)富的(de)科研资源，拥有(you)30余(yu)张(zhang)高(gao)性(xing)能GPU（如A100、A800等）及近30个高(gao)性(xing)能CPU计算节点，可(ke)充(chong)分满(man)足博士生在多尺度建(jian)模与(yu)机器学(xue)(xue)习等方(fang)向上(shang)的(de)计算需(xu)求，积(ji)极支持学(xue)(xue)生开(kai)展创新(xin)性(xing)科研工作。所有(you)录取(qu)博士生均可(ke)获得(de)奖学(xue)(xue)金(jin)资助(zhu)，目前(qian)资助(zhu)金(jin)额为(wei)每月18,760港币。诚(cheng)邀(yao)在相(xiang)关领域具有(you)良好学(xue)(xue)术背景、并对材(cai)料(liao)模拟与(yu)机器学(xue)(xue)习研究充(chong)满(man)热(re)情(qing)的(de)优秀学(xue)(xue)生加入本课(ke)题组，共同开(kai)展前(qian)沿科学(xue)(xue)探索。

开(kai)发和(he)应用(yong)多(duo)尺度(du)建(jian)模方法，研(yan)究锂离子在聚合(he)物(wu)(wu)基复合(he)电(dian)解质中(zhong)的(de)(de)溶剂(ji)化(hua)和(he)传(chuan)(chuan)输(shu)机(ji)制。构(gou)建(jian)基于物(wu)(wu)理机(ji)制的(de)(de)代理函数以快(kuai)速预(yu)测(ce)离子传(chuan)(chuan)输(shu)性能，并结(jie)合(he)机(ji)器(qi)学习优化(hua)固态(tai)电(dian)解质的(de)(de)设(she)计。

● 使用分(fen)子动力学模拟（MD）和量子化学计算(suan)（QC）研(yan)究锂(li)离子在聚合物基电解质中的(de)溶(rong)剂化结构及动力学行为；

● 构建粗(cu)粒化模型及(ji)基于(yu)物(wu)理机制的(de)代理函数，加速离子传输性能(neng)的(de)预测；

● 开(kai)发机器学(xue)习模型，提(ti)取(qu)潜在(zai)特征并优化电解(jie)质材料；

● 与高通量实验(yan)(yan)(yan)生(sheng)成的数据结合，验(yan)(yan)(yan)证(zheng)模拟(ni)结果并指导(dao)实验(yan)(yan)(yan)设计。

招生单位：香(xiang)港(gang)大学化(hua)学系(xi)

申请条件：需满足香港大学(xue)博士研究生入学(xue)要求(qiu)（如(ru)雅思(si)成绩、GPA等）。

申请材料：个人简历、成(cheng)绩单、研究计划(hua)、推荐信（2封及以上）。

截止日期：欢迎尽(jin)早申(shen)请，招(zhao)生(sheng)名(ming)额(e)有(you)限，录满为止。

有意(yi)申(shen)请(qing)者请(qing)将(jiang)申(shen)请(qing)材(cai)料发送至胡老师(shi)邮箱(xiang)ziyang1@hku.hk，邮件标题请注明(ming)“PhD Application of [SURNAME], [Given Name]”，如(ru)“PhD Application of SHEN, Qing”。

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.

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”.