Systems, Data, Simulation & Energy/en
The research group Systems, Data, Simulation & Energy (SYDSEN, pronounced "side-sin") is dedicated to advancing the field of Modeling and Simulation by developing new methods to utilize the abundant data from readily accessible Internet of Things (IoT) devices. Additionally, the research group investigates the synergies between Artificial Intelligence and Simulation to enhance both fields.
The application areas focus on cyber-physical systems, including smart factories and energy systems, with the aim of improving various performance metrics such as energy efficiency, production, and reliability. Our research contributes to optimizing the use of these systems and harnessing the benefits of IoT technology.
We take pride in being a research group that is at the forefront of this field and are always open to opportunities to collaborate with other researchers and interested parties. If you have any further questions or are interested in collaborating, please do not hesitate to contact us.
Keywords: Modeling and Simulation • Data-driven Simulation • Data Analytics • Digital Twins • Energy Efficiency • Reliability Modeling and Analysis
You can find the publications of our team at here.
|Student Assistants(HiWi)||Student Assistants (HiWi)(f/m/d) in the Research Group Systems, Data, Simulation & Energy (SYDSEN)|
|Open Ph.D. and PostDoc positions||Open PhD and PostDoc positions as SYDSEN|
|Courses in Winter Semester||Courses in Summer Semester|
|Informatik II||Modeling and Simulation|
We are open for inquiries for supervision of Master and Bachelor projects. In particular, we would supervise thesis in the following broad topics:
- Modeling and Simulation: This topic involves developing mathematical models and creating computer simulations to represent real-world phenomena or systems. A thesis in this area could focus on the development of novel modeling techniques, simulation algorithms, or the application of modeling and simulation to solve specific problems in fields like engineering, economics, or healthcare.
- Data-Driven Simulation: With the abundance of data available today, data-driven simulation focuses on utilizing data to improve the accuracy and realism of simulations. A thesis in this area may explore techniques for data integration, analysis, and validation to enhance simulation models, as well as investigating ways to leverage machine learning or statistical methods to optimize simulations based on real-world data.
- Digital Twins: Digital twins are virtual replicas of physical systems, enabling real-time monitoring, analysis, and prediction. A thesis in this area could involve creating digital twin models, developing methods to synchronize digital twins with their physical counterparts, or exploring applications of digital twins in industries such as manufacturing, healthcare, or urban planning.
- Process Mining: Process mining involves analyzing event logs to discover, monitor, and improve processes within organizations. A thesis in this area may focus on developing process mining algorithms, techniques for process discovery or conformance checking, or applying process mining to specific domains such as supply chain management or healthcare.
- Simulation and Modeling for Energy Efficiency: This topic centers on using simulation and modeling techniques to optimize energy consumption, identify energy-efficient solutions, and evaluate the impact of different energy-related strategies. A thesis in this area could involve developing simulation models for energy systems, investigating energy management algorithms, or evaluating the effectiveness of energy-efficient technologies or policies.
- Reliability Modeling and Analysis: Reliability modeling and analysis involve assessing the performance and dependability of systems to ensure smooth operations and minimize failures. A thesis in this area may focus on reliability modeling techniques, fault diagnosis methods, or reliability-based optimization approaches, with applications in fields such as manufacturing, energy systems, or critical infrastructures.
We are also interested in supervising theses that align with our research interests and involve collaborations with companies. Please contact us for more information and to discuss potential thesis opportunities.
- Prof. Dr. Sanja Lazarova-Molnar (Prof.)
- Atieh Khodadadi (Ph.D. Candidate)
- Manuel Götz (Ph.D. Candidate)
- Michelle Jungmann (Ph.D. Candidate)
- Elisabeth Lieder (Secretary)
SYDSEN welcomes visiting students from all over and seeks international partnerships.
We have hosted the following students: