Transforming Urban Infrastructure through Innovative Logistics Solutions

Vor 4 Tagen


Munich, Bayern, Deutschland Technical University of Munich Vollzeit
Company Overview

The Technical University of Munich (TUM) is a world-renowned research institution with a strong focus on innovation and entrepreneurship. Our TUM Asia Graduate College in Singapore is at the forefront of cutting-edge research, pushing the boundaries of what is possible in urban planning and construction logistics.

Job Description

We are seeking a highly motivated and talented individual to join our team as a doctoral candidate in the field of Digital Twin based Lean Construction Logistics for High-Density-Good Supply and Disposal in Urban Environments.

Required Skills and Qualifications
  • A master's degree in a relevant area like Mechanical/ Industrial/ Civil/ Transportation engineering, Logistics or equivalent
  • Enthusiasm for shaping the future of construction logistics and cargo transport with applied research
  • Theoretical knowledge or specific practical experience in process analysis, lean logistics, digital twin development, IoT-data acquisition and handling
  • Experience and interests in using modelling and simulation tools like SysML, UML, Plant Simulation or anylogic
  • Ability to work in a multicultural environment
  • Motivation to publish research papers in relevant high-ranked journals/conferences
Estimated Salary

$4,300 - $5,800 per month (SGD)

Benefits

As a doctoral candidate, you will have the opportunity to work closely with renowned professors and researchers in the field, contributing to groundbreaking research that has a direct impact on the development of urban infrastructure. You will also be enrolled as an active doctoral candidate in the TUM Graduate School, with access to comprehensive support services and resources.

Others

This position is funded by the German Academic Exchange Service (DAAD) and TUM Asia, offering a full-time, fixed-term position at the TUM Singapore Campus. The scholarship is tenable for one year in the first instance and is renewable subject to good research performance.



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