Author: Elmar Rueckert

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Rehmet Hussen

Elmar Rueckert people_student_assistants

Student Assistant at the Montanuniversität Leoben

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Short bio: Rehmet Hussen joined CPS on the 1st of October 2026.

She is studying industrial data science in her final year of her masters program at the University of Leoben. Her research interests includes embodied AI and machine learning. 

Research Interests

  • Industrial Data Science
  • Machine Learning
  • Robotics

Contact

Rehmet Hussen
Student Assistant at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria 

Email: rehmet-yeshanew.hussen@stud.unileoben.ac.at

Ing. Patrick Lipp (Technician)

Elmar Rueckert people_technician

Technician

Short bio: Mr. Lipp is supporting the three chair: Automation, Mining and CPS. He  joined the three teams in April 2026.

Research Interests

  • Electronics and PCB design and development
  • Development of Robotic Systems 

Contact

Mr. Patrick Lipp
Techniker des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1904
Email: patrick.lipp@unileoben.ac.at
Web:  https://cps.unileoben.ac.at

Aleksandr Karpov, M.Sc.

Elmar Rueckert people_accademic

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Aleksandr Karpov started at CPS on 1st of November in 2026. He is jointly supervised with the SIEMENS AG in Graz, Austria.  

Research Interests

  • Machine Learning
  • Cyber-Physical-Systems
  • Data Modeling

Contact & Quick Links

M.Sc. Aleksandr Karpov
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert.
Montanuniversität Leoben
Roseggerstrasse 11 , 
8700 Leoben, Austria 

Phone: +43 3842 402 – 1901 (Sekretariat CPS)
Email:  v.nair@fh-kaernten.at
Web Work: CPS-Page

Personal Website
GitHub
Google Citations
LinkedIn
Research Gate

Publications

Nair Vishnu Parameswaran, M.Sc.

Elmar Rueckert people_accademic

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Nair Vishnu Parameswaran is jointly supervised with Prof. Mathias Brandstötter, Head of ADMiRE Research Center, at the FH Kärnten. The Co-supervison of Nair started 1st of August in 2026. 

Research Interests

  • Machine Learning
  • Humanoid Robot Learning
  • Embodied AI

Contact & Quick Links

M.Sc. Nair Vishnu Parameswaran
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert.
Montanuniversität Leoben
Roseggerstrasse 11 , 
8700 Leoben, Austria 

Phone: +43 3842 402 – 1901 (Sekretariat CPS)
Email:  v.nair@fh-kaernten.at
Web Work: CPS-Page

Personal Website
GitHub
Google Citations
LinkedIn
Research Gate

Publications

Muslim Alaran, M.Sc.

Elmar Rueckert people_accademic

Ph.D. Student at the Montanuniversität Leoben

Short bio: Mr. Muslim Alaran started at CPS on 1st of August in 2026. 

Research Interests

  • Machine Learning
  • Robotics
  • Computer Vision
  • Humanoids

Contact & Quick Links

M.Sc. Muslim Alaran
Doctoral Student supervised by Univ.-Prof. Dr. Elmar Rueckert.
Montanuniversität Leoben
Roseggerstrasse 11 , 
8700 Leoben, Austria 

Phone: +43 3842 402 – 1901 (Sekretariat CPS)
Email:   muslim.alaran@unileoben.ac.at 
Web Work: CPS-Page
Chat: WEBEX

Personal Website
GitHub
Google Citations
LinkedIn
Research Gate

Publications

Aleyna Yilmaz (Secretary)

Elmar Rueckert people_secretariat

Short bio: Aleyna Yilmaz joined the CPS team in August 2026 and is responsible for organizational and administrative matters. She holds several diploma in business and accounting and was for three years enrolled at the Institute for Early Childhood Education. 

Research Interests

  • Cyber-Physical-Systems 
  • Modern Technologies 
  • Learning Machines and Robotics

Contact

Aleyna Yilmaz
Sekretariat des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria

Phone:  +43 3842 402 – 1901
Email:   aleyna.yilmaz@unileoben.ac.at 
Web:  https://cps.unileoben.ac.at

Batterierecycling durch KI-gestützte Objekterkennung, Robotik und Sensorfusion (BaKIRoS)

Elmar Rueckert projects_thirdparty

Themenschwerpunkt Batterieforschung

SFG Projekt 01/04/2026-31/03/2028

BaKIRoS hat zum Ziel, die Wertschöpfungskette für Batterien zu schließen, indem End-of-Life-(EoL)-Batterien zuverlässig aus gemischten Abfallströmen ausgeschleust werden. Durch den Einsatz von sensor- und KI-gestützten Verfahren können sowohl beschädigte als auch intakte Batterien identifiziert und separiert werden.

Die gezielte Aussortierung ermöglicht die Rückgewinnung kritischer Rohstoffe wie Kobalt, Nickel, Lithium und Graphit und trägt damit sowohl zur Ressourcenschonung als auch zur Reduktion von Umweltbelastungen bei.

Im Rahmen des 24-monatigen Forschungsprojekts werden innovative Ansätze zur KI- und sensorgestützten Batteriedetektion entwickelt, trainiert und systematisch evaluiert. Dabei liegt ein besonderer Fokus auf der Untersuchung und dem Vergleich unterschiedlicher Trainingsdatensätze und Modellansätze für den Einsatz in realen Recyclingprozessen.

Projektconsortium

  • Montanuniversität Leoben

    • Lehrstuhl für Abfallverwertungstechnik und Abfallwirtschaft (AVAW)

    • Lehrstuhl für Cyber-Physical-Systems (CPS)
  • SAMsoric

Fördergeber

  • Steirische Wirtschaftsförderungs­gesellschaft m.b.H. SFG

  • Zukunftsfonds Steiermark, Amt der Steiermärkischen Landesregierung

EU Industrial Doctoral Network (Reffracteur)

Elmar Rueckert projects_thirdparty

Marie Skłodowska-Curie Actions Doctoral Network (MSCA DN)

EU MSCA DN Projekt 01/09/2026 - 31/08/2029

urope’s high-temperature industries, such as steel, cement, and glass, rely on refractory materials that are critical for safe, efficient, and sustainable operations. As the EU is currently under strong Green and Digital Transitions, the refractory sector faces urgent challenges: decarbonisation, circularity, and digitalisation. In this specific industrial sector, current practices are still limited by empirical design, fragmented data, and low recycling rates, while new regulations demand traceability and reduced carbon footprint.

 

REFFRACTEUR (Digital REFractory FRAmework for a Carbon-neutral and Resilient indusTry in EURope) addresses these challenges by gathering a unique consortium spanning the entire refractory value chain, from raw materials to end-users, across academia and industry. The project will train 15 Doctoral Candidates through an innovative programme combining advanced research in materials science, process engineering, digital tools (AI, digital twins, and sustainability (life cycle assessment). Each researcher will benefit from dual academic/industrial supervision and spend at least 50% of their time in industry, ensuring strong intersectoral skills and real-world impact.

REFFRACTEUR will deliver: (1) new sustainable refractory materials with improved durability and recyclability; (2) the first unified circularity framework for refractories, integrating digital product passports and life cycle assessment; (3) validated digital twins and AI-based decision-support systems for predictive maintenance and energy optimisation; and (4) an open, standardised data backbone to accelerate innovation and knowledge transfer.

By embedding training, research, and innovation in a single European network, REFFRACTEUR will strengthen Europe’s industrial sovereignty, reduce dependency on critical raw materials, and position the EU as a global leader in sustainable, digitalised refractory technologies. The project’s outcomes will directly support the EU’s climate, circular economy, and skills agendas, creating lasting impact for industry, society, and future generations of researchers.

Projektconsortium and Details

The partners include 17 beneficiaries and 10 associated partners, see https://www.reffracteur.eu/partners/.

1 PhD or Postdoc Position – Robot Learning (open topic)

Elmar Rueckert job

The Chair of Cyber-Physical Systems at Montanuniversitaet Leoben in Austria  is offering a full-time position (100% employment) starting as soon as possible.

• Employment Type: Full-time (40 hours/week) – PhD student or Postdoc

• Salary: €3,776.10/month (Ph.D.) or €5,014.30/month (postdoc), paid 14 times per year 

• Duration: 

• PhD: up to 4 years (including completion of the doctoral degree)
• Postdoc: initially 2 years, with the option for extension

Research Focus

The positions are part of projects aiming to unlock the potential of humanoid robots in industrial environments. Our research focuses on learning-based methods for robust, adaptive, and generalizable robot skills, including:

• Whole-body control of humanoid robots in teleoperation and learning-based environments

• Robot skill learning using modern generative and reinforcement learning approaches
• Multi-modal robot learning utilizing vision, acoustic, and tactile sensing
• Simulation-based learning and sim-to-real transfer for robust deployment
• Adaptive manipulation and motion planning in dynamic and unstructured environments

This research position will focus on multiple aspects of these topics, with a special emphasis on humanoid robot skill learning of industrial applications. 

What we offer

• A dynamic and collaborative research environment in artificial intelligence and robotics

• The opportunity to develop your own research ideas and work on cutting-edge projects

• Access to state-of-the-art lab facilities

• International research collaborations and conference travel opportunities

• Targeted career guidance for a successful academic and research career

The figure below illustrates an example application of the project, focusing on learning manipulation tasks with objects on a conveyor belt. The picture above is an artwork created out of that picture from our research lab. 

Requirements

• For Ph.D. candidates: Master’s degree in Computer Science, Physics, Telematics, Statistics, Mathematics, Electrical Engineering, Mechanics, Robotics, or a related field

 
• For postdoctoral candidates: A completed Ph.D. in a topic related field 

• Strong motivation for scientific research and publications

• Ability to work independently and collaboratively in an interdisciplinary team

• Interest in writing a PhD dissertation

Desired additional qualifications

• Programming experience in C, C++, C#, Java, MATLAB, Python, or a similar language

• Familiarity with AI libraries and frameworks (e.g., TensorFlow, PyTorch)

• Strong English communication skills (written and spoken)

• Willingness to travel for research collaborations and technical presentations

Application & Materials

A complete application includes:

1. Curriculum Vitae (CV) (detailed)

2. Letter of Motivation – State if you apply for the Ph.D. or the postdoc position

3. Master’s or Ph.D. Thesis (PDF or link)

4. Academic Certificates (Bachelor’s and Master’s degrees)

Optional but beneficial:

5. Letter(s) of Recommendation

6. Contact Information for References (name, email, phone)

7. Previous Publications (PDFs or links)

Application deadline: Open until the position is filled.

Online Application via Email: Please send your application files to rueckert@unileoben.ac.at

The Montanuniversität Leoben intends to increase the number of women on its faculty and therefore specifically invites applications by women. Among equally qualified applicants women will receive preferential consideration.Research Focus