MSc. WiInf Julian Neugebauer
Photo: UHH/Neugebauer
Research associate
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Office
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Research Interests
Digital twins, IoT integration, artificial intelligence and machine learning, blockchain, and sustainability in the context of logistics.
Publications
- 2022 i.p. Blockchain in Trade Logistics, Deutscher Wirtschaftsdienst (Beck).
- i.p. The Electronic Bill of Lading from an Ecological Perspective, Suub Bremen
- 2021 Artificial Intelligence in Trade Logistics, Deutscher Wirtschaftsdienst (Beck)
- 2021 Bill of Lading via Blockchain Saves CO2, Computerwoche - CIO
- 2021 Explainable AI and the European Advantage, Informs Conference (CA, USA).
- 2021 New Ways for Bills of Lading - The Research Projects "eCONBiL " and "HAPTIK" Show Digital Options, Logistics Pilot
- 2020 Business Meets University, DUZ Transfer
- 2019 Analysis of Ocean Freight Bills of Lading and the Resulting CO2 Emissions, Suub Bremen
Background
11.2021 - today
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Research assistant & PhD student at Uni Hamburg Research in the IHATEC project TwinSim - full time Development of a digital twin for the Eurogate port terminal including simulation and simulation-based optimizations. Consideration of IoT integration and machine learning, Blockchain and ecological factors of the terminal. Funded by the BMVI. Project volume €3.6 million. |
| 10.2019 - 10.2021 |
Research associate at the University of Applied Sciences Bremerhaven Project responsibility in the research project eCONBiL - half-time. Development of a Blockchain based implementation of the Bill of Lading in collaboration with abat, DZ Bank, Kühne + Nagel, R+V Insurance and MSC using Hyperledger Fabric. CO2 emission calculations, integration via APIs. Team responsibility for ten employees. |
| 03.2019 - 08.2019 |
Intern/Bachelor Student at abat AG, Bremen Big Data and IoT connectivity - full-time. Building a Hadoop cluster with integration of Kafka and MapReduce algorithms on a Linux server cluster. Integrating IoT data within SAP cloud and storing the data in the HDFS cluster. Analysis of the data with SAP Leonardo and machine learning (Tensorflow). |