Here you find the profiles of the participating professors in the Engineering and Computer Sciences. This list is constantly updated. You can conveniently apply directly at the end of each profile: please download and fill in the application form and send it to us via e-mail (use the blue button at the end of the PDF form) by 15 February 2024. Please keep in mind to attach your CV and a publication list to the e-mail.
Important note: In case you would like to work with a researcher who has not uploaded a profile, please fill in the application form (PDF) and send it with your academic CV and a publication list to research-explorer@rs.rub.de by 15 February 2024 so that we can get in touch with the respective professor. Do NOT send any kind of application to a professor directly.
Faculty of Computer Science
Research Center Trustworthy Data Science and Security
Chair for Verification and Formal Guarantees of Machine Learning
Host's Website
Research Area:
The Chair for Verification and Formal Guarantees of Machine Learning is dedicated to advancing the safety, reliability, and trustworthiness of artificial intelligence through cutting-edge formal methods. Our research focuses on three key areas: developing secure machine learning techniques, particularly safe reinforcement learning; verifying software and machine learning models, including neural networks; and creating inherently interpretable models, such as automata and logical formulas. In light of the European AI Act, we have expanded our scope to include investigations into the security aspects of machine learning, encompassing issues like model inversion and backdoors, as well as ensuring AI alignment. By pioneering novel approaches and solutions, our group strives to foster trust and confidence in AI systems, paving the way for their widespread adoption and societal impact.
Candidate Profile:
The perfect candidate does not exist! We are looking for Postdocs with a background in machine learning (e.g., deep learning, interpretable machine learning, safe and secure machine learning) and an eagerness to delve into symbolic and formal methods. On the other hand, we seek Postdocs with experience in formal methods (e.g., constraint solving, automata theory, and logic) and the will to expand their horizons to machine learning and artificial intelligence. We welcome applicants with a proven track record in either theoretical or practical aspects of machine learning or formal methods - or those who aim to work at the interface of both. A demonstrable commitment to interdisciplinary research, coupled with critical thinking, meticulous attention to detail, independence, self-motivation, and a willingness to learn, completes the profile of our desired candidate.
Computer Science Department
Research Center Trustworthy Data Science and Security
Chair for Data Science and Data Engineering
Host's Website
Research Area:
Our research covers trustworthy machine learning, scalable ML algorithms, and interactive data exploration for various data types. We focus on large and complex data in high-dimensional data, heterogeneous networks, multivariate time series, and endless data streams. Data in our research stem from interdisciplinary research collaborations (e.g. engineering, medicine, chemistry) and industrial collaborations (e.g. automotive, sustainability, logistics).
The chair is leading and contributing to several open-source initiatives, enabling repeatability and comparability for the research community. We have organized several tutorials and workshops at major machine learning conferences and edited a special issue for the Machine Learning Journal. In the past few years, we have initiated and coordinated various education programs for “Data Science” and “Data Engineering”: On the level of university education (e.g., M.Sc. Safe AI), three graduate schools, and multiple executive education programs for industry.
Our broad research and education programs will give you the opportunity and resources to collaborate with data science students as well as interdisciplinary researchers and industrial partners in Germany.
Extended description for long-term positions: https://static.rc-trust.ai/6_Full_PostDoc_Positions_Trustworthy_Data_Science_Security.pdf
Candidate Profile:
We search for candidates in core areas of machine learning:
# Fair, Accountable, Transparent ML (FAccT ML)
# Representation Learning on Complex Data
# Explainable and Trustworthy ML
# Verifiable Predictive Analytics
We aim at both fundamental theoretical research as well as practical applications in the form of close collaboration with industrial partners. You are embedded in a creative, attractive, and internationally renowned research environment. We strive for research as a team contribution of our research group with international research colleagues in joint publications and presentations at leading international conferences.
Candidates bring in an excellent PhD in computer science, physics, mathematics or similar programs with good knowledge in statistics, linear algebra, as well as algorithms and data structures. Practical experience in machine learning algorithms is an advantage. A high degree of creativity, commitment, analytical competence, and teamwork.
Faculty of Computer Science
Research Center Trustworthy Data Science and Security
Chair for Human Understanding of Algorithms and Machines
Host's Website
Research Area:
Nils Köbis' work focuses on the human understanding of algorithms and machines, particularly in the context of corruption, ethical behavior, and social norms. His recent work extends to the domain of artificial intelligence. He holds positions at the Research Center Trustworthy Data Science and Security, University of Duisburg-Essen, and the Center for Humans and Machines at the Max Planck Institute for Human Development.
Nils' expertise lies in exploring the intricacies and implications of corruption and unethical behavior in both human and machine interactions. His contributions include co-founding the Interdisciplinary Corruption Research Network and co-hosting the KickBack - Global AntiCorruption Podcast. Using behavioral experiments, his work explores the potential of AI to foster collaboration, improve educational outcomes, strengthen interpersonal relationships, but also examines the ethical and social risks of employing AI in various roles in our society.
Candidate Profile:
The candidates should be interested in one of the following topics:
- AI and its impact on education
- ethical risks of AI interactions
- synthetic relationships between humans and conversational agents
- using AI tools to fight corruption
Our aim is to engage in both profound theoretical research and its practical applications, achieved through collaborations with researchers from various disciplines. You will become part of a dynamic, respected, and international research environment. We believe in collaborative research, involving our team and international academic colleagues, with the goal of producing joint publications and presenting our findings at premier international conferences.
Candidates bring in an excellent PhD in psychology, economics, computer science, or similar disciplines with good knowledge in experimental methods, statistics, data analyses, and ideally also knowledge of machine learning.
Faculty of Electrical Engineering and Information
Research Group Intelligent Technical Systems (Lernende Technische Systeme)
Host's Website
Research Area:
Our group focuses on core AI/ML research. More specifically, we develop algorithms for sequential learning and optimization under uncertainty and prove their characteristics, such as efficiency and convergence. We also study strategic decision-making under uncertainty in multi-agent systems and networks. The area encompasses (inverse/deep/causal) reinforcement learning, multi-armed bandits, cooperation/competition/coordination in multi-agent systems, and similar problems.
Application-wise, we focus on engineering and technical systems, especially, novel communication and computation technologies. These include joint communication and sensing, intelligent reflecting surfaces, and edge intelligence. We develop and use ML/AI methods to optimize the performance of such technologies, for example, concerning resource efficiency.
Candidate Profile:
We are excited to host talented, knowledgeable, highly-motivated, hard-working, friendly, and cooperative individuals. Besides, to work productively in our team, you need to have
• An excellent recent M.Sc. or Ph.D. degree in Electrical Engineering, Computer Science, Mathematics, and related fields such as theoretical physics,
• A solid mathematical background and the interest/motivation/patience to work on theoretical problems,
• Profound programming skills (at least MATLAB or Python, depending on the academic background),
• English proficiency.
Faculty of Electrical Engineering and Information
Chair of Microsystems Technology
Research group 2D Electronics
Host's Website
Research Area:
The Chair of Microsystems Technology deals with all aspects of micro-electro-mechanical systems (MEMS), from the initial idea to cleanroom production. Research focuses on new concepts for 2D electronics (incl. materails research), passive sensors, microactuators, micro-nano integration and system integration for indiviual applications. The chair hosts within its cleanroom the ForLab Microelectronics Bochum, which aims to develop innovative, particularly resource-efficient electronic systems based on 2D integration. Innovative, monolayer-precise deposition and etching technology at low temperatures for the production of cost-effective, flexible microelectronics and ultrasensitive microsensor technology is being developed and implemented in a 200 mm cluster system on substrates.
Candidate Profile:
Your profile:
- interest in microsystem technology, nanoelectronic, microoptics, microfluidics, RF-MEMS and / or Si-based processing, previous technological knowledge is appreciated,
- communication skills in English and / or German
- interests in sensor systems
- experience with 2D materials desirable
- PhD degree in electrotechnics, mechanical engineering, microsystem technology or corresponding fields, preferentially with skills in microsystems technology
Faculty of Mechanical Engineering
Institute for Thermodynamics and Fluid Dynamics
Chemical Recycling Group at Chair for Responsible Process Engineering
Host's Website
Research Area:
Chemical recycling of plastics has become a promising technical solution for converting today's plastic waste streams into valuable raw materials for a defossilized process industry. However, it is not only the actual conversion step that is relevant for the successful integration of chemical recycling products. The physical and chemical processing of chemical recycling products as well as the development of overall processes and their life cycle assessment are also fundamental to identifying promising technologies and bringing them to technical maturity. The Chemical Recycling Group at the Chair for Responsible Process Engineering therefore addresses strategies for the processing and upgrading of chemical recycling products, and the systematic identification of promising reintegration paths into the process industry. In addition, we design complete chemical recycling processes for various conversion technologies and evaluate them using life cycle assessment. Based on this, we develop a framework for the multi-criteria evaluation of holistic reintegration routes that enables transparent and knowledge-based comparisons between different technologies and process designs.
Candidate Profile:
We are interested in a candidate who is already somewhat familiar with the overall "chemical recycling" issue, and strongly interested in a topic that usefully complements the topics listed in the description above and below:
#1 physical and chemical upgrading of chemical recycling products
#2 model-based development of complete chemical recycling processes, incl. feed preparation and product upgrading
#3 life cycle assessment of chemical recycling processes
#4 development of a multi-criteria framework for the evaluation of entire reintegration routes
We are welcoming candidates who are interested in experimental or theoretical works, or both combined. For the entire project to work, our candidate should have a desire for intensive cooperation at eye level with different partners as well as the ability to independently organize and realize her or his own research activities.
Faculty of Mechanical Engineering
Institute for Materials
Chair of Atomic-Scale Characterisation
Host's Website
Research Area:
My current research interest is synthesizing and characterizing oxide and metallic nanomaterials for fuel cells, water electrolyzers, and battery applications. I am also keen to develop correlative multimodal approaches centered around atom probe tomography to reveal atomic-scale structural and compositional changes at electrolyte/electrode interfaces, thereby providing mechanistic insights into how electrode materials work and degrade.
In addition to electrocatalysts, I am interested in structural materials such as titanium and high entropy alloys. The research focuses on better understanding their structural instability and how nanosized metastable phases nucleate, grow, and improve the mechanical properties.
Candidate Profile:
The candidate is expected to have rich experiences in investigating electrode/electrolyte interfaces or interphases in batteries or electrocatalysts. She or he should be familiar with operando or in situ X-ray-based spectroscopy or electron microscopy. The candidate has solid knowledge of performing and interpreting electrochemical measurements (voltammetry and impedance spectroscopy). The candidate has operated projects about designing electrolytes to stabilize battery interfaces or interphases.
Faculty of Civil and Environmental Engineering
Institute for Computational Engineering
Chair of Computing in Engineering
Host's Website
Research Area:
The Chair of Computing in Engineering deals with three major research areas for the construction domain: artificial intelligence, sensor technology and visualisation, and data management. All three together form the basis for the development of construction digital twins, which is a key area of research at our chair. On the one hand, we use AI to create a data basis for digital twins from existing documents; on the other hand, we use various types of sensor technology for live data and, as a foundation, semantic web technologies are used to network the data of the digital twin and thus create a semantic digital twin. At the application level, we have already been able to establish ourselves with digital twins in the area of bridge construction, are working intensively on solutions for the industrial prefabrication of concrete components and their monitoring with digital twins, and have also implemented a digital twin workplace assessment in our offices as an example. We are therefore looking for a suitable candidate for the exchange in these three areas in order to continue to drive the integration of semantic digital twins forward.
Candidate Profile:
The candidate ideally has previous knowledge in the areas of semantic web technologies, linked building data, sensor technology, semantic description of sensor technology, integration of sensor data, semantic digital twins, and further basic knowledge in the area of data analysis and data science. The candidate should be able to work independently and also be willing to work in a flexible working environment with colleagues on-site and remotely in a team.
We are looking for a candidate who has already built up a network in the research community and would like to expand their network at the Ruhr-Universität Bochum and at our chair. In addition, the candidate should be enthusiastic about the topics presented and willing to develop them further together with our chair.
Department of Statistics
Chair of Computational Statistics
Host's Website
Research Area:
Probabilistic (Bayesian) approaches to statistics and machine learning have become increasingly popular in recent years due to new developments in probabilistic programming languages and associated learning algorithms as well as a steady increase in overall computing power. Probabilistic programming languages make it easier to specify and fit Bayesian models, but this still leaves us with many options regarding constructing, evaluating, and using these models, along with many remaining challenges in computation. My overarching scientific goal is to develop a principled Bayesian workflow that comprises the whole scientific process from design of studies, data gathering and cleaning over model building, calibration, fitting and evaluation, to the post-processing and statistical decision making. As such, we are working on a wide range of research topics related to the development, evaluation, implementation, or application of Bayesian methods. For more details, see https://paul-buerkner.github.io/research/
Candidate Profile:
Candidates should be excited about Bayesian statistics and/or probabilistic machine learning. The rest we will figure out together. :-)
Apply (download application form)
Department of Statistics
Research Center Trustworthy Data Science and Security
Chair of Uncertainty Quantification and Statistical Learning
Host's Website
Research Area:
The group's research interests broadly lie at the intersection of machine learning and traditional statistical methods. We are particularly working on uncertainty quantification and trustworthyness using Bayesian learning. You can find a selection of topics that we are working on here: https://kleinlab-statml.github.io/research.html
Candidate Profile:
Requirements: Completed Master and PhD (preferably with very good marks) in Statistics, Mathematics, or related field with specialisation in Statistics, Data Science or Mathematics; a strong background in the following fields: Bayesian computational methods, spatial models and inference therein, variational inference, density regression. Furthermore, a thorough mathematical understanding; substantial experience in scientific programming with R, Matlab, Python, C/C++ or similar; very good communication skills and team experience, proficiency of the written and spoken English language (German is not obligatory).
You can find more information here:
https://github.com/kleinlab-statml
https://twitter.com/KleinSLab
https://scholar.google.de/citations?user=upS2UTIAAAAJ&hl=en
Apply (download application form)
Research Academy Ruhr (RAR) is the cross-university platform of Ruhr-Universität Bochum, TU Dortmund University, and the University of Duisburg-Essen for promoting early career researchers in the University Alliance Ruhr (UA Ruhr).
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