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关于发布华晨宝马有限公司校企联合培养博士研究生2022年课题的通知

发布时间:2021-08-23 作者:

各学部(学院):


今年华晨宝马有限公司校企合作联合培养博士生项目共有6个2022-2026届科研课题(详情见附件):



华晨宝马博士生研究课题

副课题或具体内容

技术重点

 研究所属领域
(包含但不限于)


Sustainability, Energy, CO2 Management: Deep Learning Methods of Production Energy Optimization

深度学习方法在生产系统能源优化方面的研究


1. Build CO2 footprint model for finished vehicle logistics and supply chain logistics, applying state-of-art optimization methods on reducing logistics CO2 footprint.

2. Build energy model for key processes in Press shop, Body shop, Paint shop, develop deep learning methods and applying methods to optimize PUE of key processes in production.

1. Simulation pipeline modeling of manufactering process.

2. Deep learning methods of optmization control parameters

081200 计算机科学与技术 Computer Science and Technology

0811J1 机器人科学与工程 Robot Science and Engineering

Advanced Industrial Data Mining and Data Value Stream Analytics Technology

先进的工业数据挖掘和数据价值流分析技术

1. Research on efficient frameworks for big industry data processing

2. Research on the new data analytic approach to lower the entry level

3. Research on  data value stream mapping methodology among complex production chain.

New technology & algorithm for data value stream analytics

080800 电气工程 Electrical Engineering

081200 计算机科学与技术 Computer Science and Technology


Base on Multiple Trading Partner Behavior Analysis To Research Automobile Industry Receivable and Payable Credit Risk & Application

基于大数据和行为分析学,调研汽车行业贸易伙伴信用风险与智能应用

(课题内容具体待修订)





Non-Series Material (Spare Parts/Tools/Consumables) Business Model

非量产物料业务模式与数字化应用


1. Analyze BBA as-is process, planning and system logic, reconstruct the business model and process from the perspective of the supply chain based on historical preventive maintenance data.

2. Use operational data to transparentize the real daily logistics operations, capture and set key business characteristics, and establish statistical models to describe operations and plans.

3. Keep tracking all negative incident in preventive maintenance and logistics area, focusing on backtracking scenarios such as excessive lead time and out of stock, in-depth analysis of business logic loopholes, and establish a spare parts supply chain database to provide valuable data for machine learning to handle the dynamic demand for spare parts and build an AI supply chain platform.

4. Exploit dependencies between maintenance strategy and supply strategy.

5. See what can be learned from solutions for this kind of problem in other industries.


Build up the model for machine learning regarding dynamic demand for spare parts.


081200 计算机科学与技术 Computer Science and Technology

081203 计算机应用技术Computer Applied Technology


Sustainability- Recycling Material in Plastic Parts and CO2 Emission Reduction

可持续性-回收塑料在汽车零部件中的应用及CO2减排

1. Introducing the recycling plastic material into BMW components is one major measure in this working package.

2.Deeply research the recycled material applications in the exterior and interior plastic parts, such as PC, ABS, PP, PA66 and etc.

3.Deeply research the recycled material in chemical and physical properties, chemical reaction mechanism and also plastic material property change.

4.Deeply research the recycled material segment(such as PIR and PCR), manufacturing process, and also the latest development concept of recycled material.

Seeking the technical solutions to achieve recycled material ratio target for current and new project, and then achieve CO2 emission reduction per vehicle.

070303  有机化学 Organic Chemistry

0805Z1 高分子材料 Polymer Material

Correlation Study of Energy Efficiency and Cost Impact for Manufacture

能源效率和成本控制的关系

1. Energy structure for the supply chain.

2. Cost impact to energy structure.

3. CO2 reduction strategy based on energy reduction, new energy implementation, digitalization to optimize the efficiency, circularity.

4. Potentials for energy transfer to green energy.

Buid up the model for the energy efficiency and cost effiency.

0807Z1 能源与环境工程 Energy and Environmental Engineering

083002 环境工程 Environmental Engineering




请有意向与华晨宝马合作,共同培养高端创新人才,并将自己的科研课题在华晨宝马深入开展的教授与我们联系。如教授们想对这些课题进行深入的技术方面的了解,可直接与负责课题的技术专家取得直接的联系。如教授有其他希望与华晨宝马合作的科研课题,请将相关信息填写完整(见附件),我们将帮助其与公司业务部门进行沟通并建立联系。


时间要求:8月31日前


项目背景介绍:

为加强校企合作,经学校与华晨宝马的共同努力,我校于2018年5月正式与华晨宝马签订联合培养博士生项目协议。该项目旨在为汽车行业培养适应跨国公司要求的青年技术专家。项目采取双导师制。高校导师将指导学生进行项目研发并撰写论文,确保理论和学术水平;企业导师将在项目研发和在岗培训中为学生提供帮助和指导。公司将为学生提供必要的实验条件,并协助将理论转化为实践成果。目前,我校已有6位老师及6名学生参与该项目,其中三名博士生毕业。该项目也是今后我校工程博士培养模式的探索方向。

参加华晨宝马联合培养博士生项目的导师会获得华晨宝马有限公司以横向课题合同形式提供的2.5万元/生/年的技术服务费(不超过10万元/生)。在招生时占用导师招生名额,导师需为学生正常缴纳博士研究生培养费。


联系人:

华晨宝马有限公司/薛艳

联系电话:024-84559636

邮箱:Joanna.Xue@bmw-brilliance.cn

研究生院/莎日娜

联系电话:0411-84706321

邮箱:sharina@dlut.edul.cn


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