Our 3D Geometry unit conducts research on estimation problems in multi-view geometry, such as structure-from-motion problems, positioning problems, and self-calibration. The 3D motion unit focuses on reconstructing the movements of humans and animals in 3D. The remote sensing unit works on predicting forest fires using data from remote sensing. Our visual learning theory unit develops machine learning methods conditioned by statistical, geometric, or physical constraints. The visual perception unit studies the learning of perception for seeing robots, for example, under motion blur. An important part of our work is estimating uncertainty in perception.

As a postdoctoral researcher, you are expected to independently drive your own research within the unit. As a researcher, you will also supervise PhD students, contribute to international research projects, and teach. The results of your research will fall under the teacher exemption, which means that you will own the rights yourself.

As a postdoctoral researcher, you will primarily conduct research. Teaching may also be included in the tasks, but up to a maximum of one-fifth of the working time.

We are looking for a motivated postdoctoral researcher in polymer chemistry with a focus on green materials from lignin. You will work on developing new green chemical modifications of lignin for biocomposites in collaboration with research groups at Linköping University and KTH. The project, which is connected to the Wallenberg Wood Science Center, offers a unique opportunity to contribute to sustainable material solutions from forest resources. Your research will play a crucial role in shaping the environmentally friendly materials of the future.

The position is for a research project in the Coated Nuclear Fuel Rods (FurCoat) consortium, a collaboration between Linköping University, Uppsala University and Chalmers University of Technology. The focus is on computational material design of novel coating alloys for use in demanding environments, including nuclear fuel rods for use in nuclear power plants. The aim of the postdoctoral positions is to strengthen the research on electrochemical concepts and fundamentals by performing first-principles material simulations to understand oxidation and corrosion of refractory alloys using Ellingham and Pourbaix diagrams. The project is linked to the conceptual development of the HADB database (https://hadb.funmat-ii.se/) and therefore all derived data will be stored and visualized there.

There is currently a lack of reliable estimates of population densities for several Swedish ungulates. In this project, you will be part of developing a novel Bayesian framework that integrates multiple data sources (e.g., hunting harvest, ungulate-vehicle collisions, direct observations) to estimate population densities at multiple spatial levels. The position is part of a governmental assignment to develop methods for wild boar density estimation, but you will also work on other ungulates, primarily roe and fallow deer.

As postdoc, you will principally carry out research. A certain amount of teaching may be part of your duties, up to a maximum of 20% of working hours.

This postdoc position is part of the project “Scientific data analysis and visualization via topology guided feature extraction and comparison” funded by the Swedish Research Council. The goal of this project is to develop new methods for explorative visual analysis of large feature-rich datasets derived from simulations or experiments. In recent years, feature-based analysis methods rooted in the strong mathematical foundations of topological data analysis (TDA) techniques have shown a lot of promise for addressing this challenge. The first step in this process is feature extraction, wherein hidden substructures in the data are identified and extracted using multi-scale feature descriptors based on the potent data abstraction capabilities of TDA. However, the primary emphasis of this project lies in the subsequent step of this visual data analysis pipeline, namely feature comparison. This is an active area of current research with many open questions. Efficient feature comparison methods would enable various downstream analysis tasks such as data summarization, feature tracking, and ensemble analysis.

Your primary research task will be to develop new algorithms as well as implement practical efficient solutions for computation and comparison of features using TDA-based techniques. An equally important task will be the application of the developed techniques to real scientific data provided by our collaborators with expertise in theoretical chemistry and physics. We envision the novel data analysis and visualization techniques developed during this project will be particularly useful for gaining insight into complex multi-dimensional data generated through workflows rooted in quantum mechanics.

As postdoc, you will principally carry out research. A certain amount of teaching may be part of your duties, up to a maximum of 20% of working hours.