Research Explorer Ruhr: Hosts and Application

Faculty of Computer Science
Chair for Algorithm Engineering

Research Area:

The research of the chair "Algorithm Engineering" focuses on algorithms with an emphasis on computational geometry and application-driven algorithm design. We study algorithmic solutions that are both practical and provably efficient, that is, algorithms with provable guarantees on the running time, space usage and quality of the result. We therefore work on practical as well as fundamental algorithmic questions. To obtain meaningful guarantees for the algorithms, we develop algorithms that explicitly handle common characteristics of today's data, which is typically heterogenous, imprecise and in motion.

On the side of the theory of geometric algorithms, our main interests lie in the design and analysis of algorithms for fundamental geometric problems under input assumptions that match common properties of data. A key in this line of research is to combine theoretical algorithm development with empirical methods. We are particularly interested in algorithms for curves and geometric graphs. On the application side, we focus on the algorithmic foundations of analyzing spatial data, which are intrinsically geometric. One type of data we focus on is movement data.

Candidate Profile:

The candidate should have a strong background in Algorithms and interest in Computational Geometry with publications at venues like the International Symposium on Computational Geometry (SoCG), the Symposium on Discrete Algorithms (SoDA) and the European Symposium on Algorithms (ESA). Ideally, the topic of the PhD should already have been in the area of Computational Geometry. Expertise in algorithm theory is essential, interest in practical algorithm design is desirable.

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Faculty of Computer Science
Institute of Neural Computation
Research Group Theory of Neutral Systems

Research Area:

Our goal is to develop trustworthy and explainable deep learning systems. To this end, we design intuitive and interpretable models and data representations in areas such as RL, object detection and NLP. Our focus is on human-centered design in order to build safe and fair AI-powered applications.
Pavlos Rath-Manakidis works on explainable features in deep object detection models. They form a representation which allows end-users to understand weak points in the model, to calibrate their trust toward it and to refine it. Pavlos also supervises a project about natural language explanation generation that can be used as a starting point for work with large language models in human-AI-collaboration. Moritz Lange develops explainable and transferable representation learning techniques in the context of reinforcement learning, in order to make RL solutions more interpretable and trustworthy.
For future work, we are also interested in interaction design / UX during training, maintenance and work with neural models, algorithmic approaches for improving the collaboration of humans and AI models in areas such as explainability, model steering and human-feedback driven machine learning.

Candidate Profile:

The perfect candidate has obtained a very good PhD in mathematics, computer science, engineering, or a related field. We are looking for a person with a solid knowledge in Artificial Intelligence / Machine Learning, grounded in solid programming experience (especially in Python) and mathematical skills. Interpersonal competencies such as good organizational and communicative abilities will be required to leverage and excel in our complex and interdisciplinary research environment. Since we actively collaborate with and constantly reach out to the private sector (especially in the Ruhr region), an ideal candidate should have an aptitude for building professional collaborations. Good command of the German language is not strictly necessary, but would be beneficial. We are looking for an engaged researcher who stays on top of current developments in AI and wants to develop an original, independent research agenda in one of the research areas mentioned above.

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Faculty of Computer Science
Institute of Neural Computation
Research Group Theory of Neutral Systems

Research Area:

We are interested in understanding brain functions related to information processing, primarily visual information and memory. For this purpose, we work in the intersection between computational neuroscience and artificial intelligence, i.e., we model the hippocampus and the visual cortex on a system level using advanced machine learning methods. In particular, we model and simulate the following:

• the transformation of visual information as it passes through the visual system
• the interplay between the different types of memory during storage, encoding, and retrieval
• different aspects that affect memory, like forgetting, attentional selection, and the
  social context

Candidate Profile:

• very good Ph.D. in cognitive science, mathematics, computer science, engineering, or a related field
• solid knowledge of Computational Neuroscience and/or Machine Learning
• potential to develop an original independent research agenda in one of the abovementioned research areas

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Department of Electrical Engineering and Information Technology
Institute for Energy Systems, Energy Efficiency and Energy Economics (ie3)
Research Group Optimization and Control

Research Area:

The optimization and control group at the Institute for Energy Systems, Energy Efficiency and Energy Economics (ie3) comprises members from five different nations. We conduct fundamental research at the intersection of control engineering, systems theory and computer science. We focus on optimization-based and data-driven methods to control and operate cyber-physical systems in the presence of uncertainty and limited computational resources. Currently, we actively investigate the following topics:

• optimal & model predictive control,
• data-driven control & reinforcement learning,
• distributed optimization,
• dissipativity-based system analysis, port-Hamiltonian systems, and
• stochastic systems and Gaussian processes for control.

We combine fundamental research with lab experiments. Considered applications range from multi-energy systems, power systems, process systems to mechatronics and climate economy.

Candidate Profile:

You strive to boldly go where no-one has gone before? You are intrinsically interested in systems and control, feedback design or numerical optimization? You have a background in one of the following method areas?

• numerical optimization and/or optimal control
• model predictive control
• uncertain and stochastic systems
• data-driven control, Gaussian processes
• dissipativity and port-Hamiltonian systems
• distributed optimization

Then the optimization and control group @ ie3 would be happy to get to know you! It is not crucial whether your background is in engineering (mechanical, electrical, or chemical) or applied mathematics and computer science. The most crucial aspect is your open mindedness. Indeed our projects are very much method driven and the applications span a wide range from energy systems, wireless communications to mechatronics. We look forward to optimize with you!

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