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Laser powder bed fusion (LPBF) of metals is a process where three dimensional parts are manufactured by sequentially spreading thin layers of metal powder feedstock and melting portions with a laser. The process involves high cooling rates (up to 106 K/s) which lead to a very peculiar microstructure characterized by a bimodal grain size distribution. Fine grains areas are clearly visible in some regios, identified as the meltpool boundaries, however columnar and elongated grains are present in other areas of the sample. Depending on the final application, one microstructure is preferable over the other, therefore the understanding of how it is formed and control its evolution is extremely relevant for industrial applications. - Engineering and Technology
- Bachelor Thesis, Master Thesis, Semester Project
| Agri-environmental schemes are important to promote biodiversity-friendly farming practices. This thesis aims to propose a result-based scheme that uses machine learning to predict species diversity. - Agricultural, Veterinary and Environmental Sciences
- Master Thesis
| Laser powder bed fusion (LPBF) of metals is a process where three dimensional parts are manufactured by sequentially spreading thin layers of metal powder feedstock and melting portions with a laser. Byproducts of the process, such as fume and spatter, originated by the dynamics of the melt pool, can fall onto the powder bed or interact with the laser and potentially cause material defects in the parts. In this project, the student will conduct experimental builds and investigate to what extend the byproducts generation impact on the process robustness. - Engineering and Technology
- Bachelor Thesis, Master Thesis
| Translation averaging is the problem of estimating the global 3D translation of a set of cameras given their orientations and a set of relative translations estimated from image pairs. Translation averaging is a cornerstone in global Structure-from-Motion algorithms. This project is about designing a deep network that iteratively removes incorrect relative translations to estimate the global ones.
- Computer Vision
- Master Thesis, Semester Project
| Predictive maintenance is critical in complex industrial systems as it aims to reduce maintenance costs and prevent unplanned downtime by detecting anomalies before they lead to system failure. Anomaly detection is a central component of predictive maintenance and involves identifying deviations from normal system behavior. In electrical distribution systems, one avenue to anomaly detection is to derive an anomaly score from state estimation. State estimation is an existing data processing algorithm for converting redundant phasor meter readings and other available information into an estimate of the state of an electric distribution system. Conventional state estimation (CSE) estimates the state with the iterative Newton-Rhapson method based on a set of power flow equations. CSE is built around a numerical solver for a set of equations that are derived from a schematical representation of the industrial asset.
Implementing accurate distribution system state estimation (DSSE) faces several challenges, among them is the lack of observability and the high density of the distribution system, convergence issues, and computational time. While data-driven alternatives based on machine learning models show to be a better choice, they suffer from the lack of labeled training data and do not inherently learn the underlying system physics. Because of that reason, in this thesis, we focus on the sim-to-real transfer of physics-enhanced neural state estimation. The goal is to improve DSSE by pre-training a physics-enhanced model on data from extensive existing simulator packages.
We aim to use graph neural networks (GNNs) which have emerged as a promising model for DSSE. GNNs encode graph structures by aggregating features of neighboring nodes, which allows them to capture both local and global structures of the graphs. Real-time processing with data-driven state estimators is still a challenge, but GNNs are a promising approach as they perform well with a comparatively small number of neurons and layers.
- Electrical Engineering
- Master Thesis, Semester Project
| The goal of this project is to develop an end-to-end differentiable global structure-from-motion algorithm. - Computer Vision
- Master Thesis, Semester Project
| The project aims to compare and evaluate different sensing technologies for contextual intelligence that can detect and classify the presence and movement of people at workplace- or room-level. The current implementation, using infrared sensors, shall be compared to novel ultrasound time-of-flight sensors that are still not commercially available. The project is interested in the achieved tradeoff between accuracy, power consumption, and system limitations. - Digital Systems, Electrical and Electronic Engineering, Environmental Technologies
- Bachelor Thesis, Energy Harvesting (PBL), Firmware (PBL), Machine Learning (PBL), Master Thesis, Microcontroller (PBL), PCB Design (PBL), Semester Project
| To improve using Open Research Data in the field of urban drainage for further research, analysis or interpretation, we are looking for a data interoperability expert, who can support us in transferring existing geospatial standards from established communities, e.g. InfraML and WaterML2.0, to urban water observations. In addition, we want to break new ground in improving the interpretation of observations by providing dashboard-type previews of datasets to explore the quality and (re-)usability of data.
The researcher will work at the interface of data operability and environmental engineering. The work contributes to the integration across boundaries of not only different disciplines but also policy and practice, by advancing the development of water interoperability standards and developing methods and tools to explore collected datasets, such as flexible dashboarding solutions, for example Metabase, Streamlit or Jupyter notebooks. - Environmental Engineering, Environmental Sciences, Geomatic Engineering, Interdisciplinary Engineering, Soil and Water Sciences
- Post-Doc Position
| The goal is the develop an algorithm that builds a pose graph from an unordered set of images as fast as possible. This is achieved by, first, building a minimal spanning tree from the images exploiting predicted similarity scores. Then the spanning tree is populated with additional edges until the pose uncertainty falls below a threshold in each vertex. This procedure is very important for Structure-from-Motion algorithms where the first step is generated such pose graphs. - Computer Vision
- Master Thesis, Semester Project
| The thesis aims to develop a new bio-inspired robot with a thruster-based adhesion to climb uneven surfaces. - Intelligent Robotics, Robotics and Mechatronics
- Master Thesis
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