一、团队简介

宁波东方理工大学（暂名）智能多尺度过程工程团队是一个多元化的研究小组，专注于化学工程领域内先进多尺度算法和人工智能算法耦合开发与应用。团队负责人Donovan Chaffart (袁野)博士，东方理工大学（暂名）助理教授，毕业于滑铁卢大学，获得化学工程博士学位。Donovan Chaffart博士具有9年以上化工领域模型开发与应用经验，尤其擅长人工智能辅助多尺度模型的开发与应用。袁月博士，宁波数字李生(东方理工)研究院副研究员，获得中国与加拿大双博士学位，于2013年起开始从事化学工程与智能多尺度数值模拟计算交叉学科研究，拥有10年以上化工领域数值模拟技术研究与开发经验，擅长结合理论与统计学算法开发符合非理想实际环境的多尺度研究与预测模型。

团队致力于开发新型、可信赖的人工智能辅助混合多尺度模型，以深入探究化工系统机制，拓展人类在该领域的知识边界。团队的核心关注点包括智能信息互通的多尺度混合模型开与应用，符合实际非理想环境的化工体系模型开发与应用以及透明、可信赖的人工智能技术化工转化应用等。加入我们的团队，你将有机会与领域内专家合作，为实现相关的研究目标做出贡献，并为你的学术道路奠定坚实的基础。

二、招聘岗位与岗位职责

1.招聘岗位：

研究助理教授/助理研究员

2.研究方向：

（1）智能互通多尺度模型开发与应用

（2）化工可靠人工智能应用：透明度探索与分析;

（3）人工智能辅助流化系统的精密过程监测和控制;

（4）超疏水表面与表面附着液滴扩散行为的多尺度/混合模型开发与应用；

（5）化学工业数据的预处理和分析;

（6）知识嵌入的混合多尺度模型开发与应用;

（7）知识嵌入人工智能的分子尺度模型研究与应用;

（8）移动界面动力学蒙特卡罗模拟。

3.岗位职责：

（1）独立承担课题的开展和推进，整理分析实验数据；协助实验室建立研究体系；

（2）协助PI指导课题开展与实验工作；配合做好科研项目申请；参与管理工作；

（3）撰写论文及专利，参与学术会议交流，协同做好学术合作及科研转化等。

三、岗位申请条件

1.爱国守法，遵守学校规章制度，热爱教育事业；言行雅正，恪守学术规范；自尊自律，秉公平诚信；

2.身心健康，在海内外知名高校、研究机构获得博士学位；博士研究生期间有参加高水平研究项目经历，并取得较好的研究成果；

3.具有化学工程或计算机科学等相关专业研究背景，具备深厚的专业理论知识和实践经验；

4.熟悉智能互通多尺度建模、人工智能、流体力学、界面科学等相关领域的技术和方法；

5.具备较强的数据分析能力和编程能力，熟悉常用的数据处理和分析工具；

6.具有团队合作精神和良好的沟通能力，能够与其他部门协作完成跨学科项目；

7.对化学工程领域的人工智能应用充满热情，具有持续学习和探索新技术的意愿。

四、薪酬待遇

1.参照国内外大学相应职位的薪酬标准，为获聘的优秀人才提供有竞争力的、能够使其安心工作的薪酬福利待遇。

2.科研、职业规划及项目申请的一对一指导。

五、应聘方式（材料由学部、课题组根据需要确定）

1.申请材料：

（1）个人简历（中英文）；

（2）个人陈述：自我评价，突显自我的支撑性材料；

（3）推荐信：两封，同领域专家，需联系方式；

（4）博士学位等的证明材料。

2.投递方式：

请合并所有申请材料为一份PDF文件，发送到邮箱：drgchaff@eitech.edu.cn，并抄送人力资源部邮箱：Rfaculty-hr@eitech.edu.cn。文件名与邮件标题均为：“应聘岗位+姓名+招聘信息来源”。您提交的材料享隐私保密。

3.课题组会尽快评定，初审通过者将收到E-mail或电话通知后续面试事宜。本招聘信息自发布之日起长期有效。

I   Group/PI Introduction

The Intelligent Multiscale Process Engineering Group at the Eastern Institute of Technology (EIT, tentative name) is a diverse research group aiming to study the development and implementation of numerical modelling methods, high-quality multiscale simulations, and artificial intelligence techniques for a variety of different Chemical Engineering applications. The team is headed by Dr. Donovan Chaffart (袁野), an Assistant Professor at EIT who graduated from the University of Waterloo with a PhD in Chemical Engineering. Dr. Chaffart is an expert in the fields of empirical modelling, hybrid multiscale modelling, and artificial intellicence (AI) algorithm development for Chemical Engineering applications, with over 9 years of experience in this field. The group is additionally led by Dr. Yuan Yue, an Associate Research Professor at the Ningbo Institute of Digital Twin within EIT who received a dual doctorate in Chemical Engineering from both Tianjin University and the University of Waterloo. Since 2013, Dr. Yuan has been engaged in both interdisciplinary research and multiscale numerical simulation development within Chemical Engineering. She has garnered more than 10 years of experience within the research and development of numerical simulation technology in the chemical industry, and she is an expert at developing multi-scale optimization and prediction models in line with non-ideal practical environments by combining theory and statistical algorithms.

The team is committed to developing novel reliable AI systems and hybrid multiscale models to capture a wide variety of different chemical processes and further advance the bounds of human knowledge within these fields. The core concerns of the team include the development of hybrid interconnecting multiscale models, the development and implementation of novel modelling methods, and the development and incorporation of transparent AI into Chemical Engineering projects. As part of our team, you would have the opportunity to contribute to this reality and establish your own roots within academic research.

II      Recruitment Position and Job Responsibilities

II.1    Recruitment position:

Assistant Research Professor

II.2    Research direction:

II.2.1      Intelligent Intercommunicating Multiscale Modelling

II.2.2      Reliable AI Applications in Chemical Engineering: Transparency Exploration and Analysis

II.2.3      AI-assisted Precise Process Monitoring and Control of Fluidization Systems

II.2.4      Multiscale & Hybrid Modelling for Sessile Droplet Spreading and Superhydrophobic Surface Development

II.2.5      Preprocessing and Analysis of Industrial Generated Data

II.2.6      Physics-informed Hybrid Multiscale Modelling in Chemical Engineering Applications

II.2.7      Physics-informed AI for Molecular-Scale Modelling Applications

II.2.8      Moving Front kinetic Monte Carlo Simulations of Moving Interface Systems

II.3    Job responsibilities:

II.3.1      Independently establish research projects within the scope of the group’s research topics as approved by the PI.

II.3.2      Assist the PI to guide project development among the rest of the group; Cooperate with scientific research project applications; Participate in the management of the research group

II.3.3      Write papers and patents, participate in academic conferences and exchanges, and coordinate academic cooperation and scientific research transformation.

III   Recruitment Requirements

III.1   Must have obtained a related engineering/computer science PhD degree from an accredited international or domestic university; must have engaged in high-level research projects during doctoral degree and achieved good research results.

III.2   Must have a research background in Chemical Engineering, Computer Science, or a related engineering field; must have practical experience conducting professional research.

III.3   Must be proficient with python, MATLAB, or a related programming language.

III.4   Must have good spoken and written communication skills in English; good communication skills in Chinese is a bonus

III.5   Must have familiarity with the technologies and methods in the fields of multiscale modelling, artificial intelligence, fluid mechanics, interfacial science, or a related discipline.

III.6   Must have strong data analysis and programming abilities; must be familiar with commonly used data processing and analysis tools such as Computational Fluid Dynamics, kinetic Monte Carlo, Molecular Dynamics, Artificial Intelligence, etc.

III.7   Must have good team spirit; must be able to cooperate with other departments to complete interdisciplinary projects.

III.8   Must have enthusiasm for the development of first-principles models and/or statistical artificial intelligence in the field of Chemical Engineering; must have a willingness to continuously learn and explore new technologies.

III.9   Must have a background in conducting research.

III.10      Must be in excellent health, both physically and mentally.

III.11      Must have no criminal record; must abide by all legal and school laws.

III.12      Must have a passion for learning, teaching, and conducting research.

IV   Compensation & Benefits

IV.1  This job will provide a competitive salary and welfare benefits with respect to the salary standards of corresponding positions in universities both within China and abroad.

IV.2  One-to-one guidance on scientific research, career planning and project application.

V      Application Process

V.1     Application materials:

V.1.1      Your CV, in both English and Chinese

V.1.2      Cover letter, research experience, teaching philosophy, and any other materials that can show your academic competencies.

V.1.3      Two letters of recommendation from academic experts within the same field (contact information required)

V.1.4      Proof of doctoral degree

V.2     Applicant information delivery:

Please combine all of the requested application materials into a single PDF file. Send the full application file to drgchaff@eitech.edu.cn and cc the EIT Human Resources Department: Rfaculty-hr@eitech.edu.cn. The PDF document and email subject should be titled as follows:
"Applied position + name + recruitment information source". The materials you submit will be kept confidential.

V.3     The research team will evaluate your application as soon as possible, and those who pass the preliminary examination will receive an e-mail or phone call to inform them of the follow-up interview. This recruitment information is valid from the date of publication.