Author: Elmar Rueckert

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Multi-modale, taktil-visuelle Robotergreifsysteme für industrielle Anwendungen (MUTAVIA)

Elmar Rueckert projects_thirdparty

Schlüsseltechnologien im produktionsnahen Umfeld 2024

FFG Projekt 03/2025-02/2028

Unsere Forschung entwickelt intelligente Roboterhände, die fühlen können – ähnlich wie menschliche Hände. Mit innovativen Sensoren auf Basis von nachhaltigen Materialien wie Cellulose und Nanocellulose schaffen wir Lösungen, die nicht nur Druck und Temperatur spüren, sondern auch Schadstoffe und biologische Gefahren erkennen können.

Diese Technologie wird speziell für schwierige Aufgaben in der Industrie entwickelt, z. B. beim Sortieren von Abfällen oder empfindlichen Materialien, die bisher nur Menschen bewältigen konnten. Durch den Einsatz von Robotern in gefährlichen oder anstrengenden Arbeitsumfeldern schützen wir Menschen und verbessern die Arbeitsbedingungen.

Mit unseren nachhaltigen, langlebigen und präzisen Sensoren setzen wir neue Maßstäbe in der Automatisierung. Das stärkt nicht nur die heimische Wirtschaft, sondern bringt auch Innovation und Sicherheit für die Gesellschaft.

Projekt Consortium

  • Montanuniversität Leoben
    • Forschungs- und Innovationsservice Montanuniversität (Administrativer Koordinator)
    • Lehrstuhl für Automation und Messtechnik
    • Lehrstuhl für Cyber-Physical-Systems
  • FreyZein GmbH
  • Saubermacher Dienstleistungs AG

Fördergeber

  • Österreichische Forschungsförderungsgesellschaft mbH (FFG)

Project Summary

Our research is developing intelligent robotic hands that can feel – just like human hands. Using innovative sensors based on sustainable materials such as cellulose and nanocellulose, we are creating solutions that not only sense pressure and temperature, but can also detect pollutants and biological hazards.

This technology is being developed specifically for difficult tasks in industry, such as sorting waste or sensitive materials that previously only humans could handle. By using robots in hazardous or strenuous working environments, we protect people and improve working conditions.

With our sustainable, durable and precise sensors, we are setting new standards in automation. This not only strengthens the domestic economy, but also brings innovation and safety to society.

Poster

TBA

Other

TBA

Mag. Eva Anderson (Secretary)

Elmar Rueckert people_secretariat

Secretary

Short bio: Ms. Eva Anderson joined the CPS team in February 2025 and is in charge of organizational and administrative matters. She earned her degree in commerce at the Vienna university of economics and business.

Research Interests

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

Contact

Mag. Eva Anderson
Sekretariat des Lehrstuhls für Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1907
Email:   eva.anderson@unileoben.ac.at 
Web:  https://cps.unileoben.ac.at

1 Stelle im Sekretariat (m/w/d) – 12.Dezember 2024, RefID: 2411APC

Elmar Rueckert pastjob

Die Montanuniversität Leoben ist eine moderne Lehr- und Forschungsstätte und bietet sehr gute
Voraussetzungen für Karrieren in wissenschaftlichen und nichtwissenschaftlichen Berufsfeldern.
Am Department Product Engineering – Lehrstuhl für Cyber-Physical-Systems gelangt die Stelle im
Sekretariat (m/w/d) zur Besetzung.

Für diese Position ist gemäß dem Kollektivvertrag für Arbeitnehmer*innen (m/w/d) der Universitäten die
Verwendungsgruppe IIb (€ 2.460,10 brutto, 14x jährlich) vorgesehen, tatsächliche Einstufung erfolgt
lt. anrechenbarer tätigkeitsspezifischer Vorerfahrung.

Vorgesehener Dienstantritt: ehest möglich
Beschäftigungsdauer: unbefristet
Beschäftigungsausmaß in Wochenstunden: 20 h (mit der Option auf Erhöhung des Beschäftigungsausmaßes ab Jänner 2026)

Aufgabenbereich

  • Unterstützung der Lehrstuhlleitung bei der Verwaltung von Terminen und Korrespondenz in
    deutscher und englischer Sprache
  • Allgemeine Verwaltungstätigkeiten
  • Büroorganisation, Korrespondenz, Parteienverkehr
  • Mitarbeit bei Projektkoordination
  • Bestellung und Zahlungsabwicklung über SAP
  • Organisation der Lehre
  • Betreuung der Studierenden und Vortragenden
  • Parteienverkehr

Anstellungsvoraussetzungen

  • Abgeschlossene kaufmännische Ausbildung (Handelsschule oder ähnlich) mit mehrjähriger,
    einschlägiger Berufserfahrung
  • Kaufmännische, fachliche und organisatorische Kenntnisse
  • Sehr gute EDV-Kenntnisse (MS Office)
  • Sehr gute Deutsch und Englisch Kenntnisse (Wort und Schrift)
  • Männliche Bewerber nur nach abgeschlossenem Präsenz-/Zivildienst

Erwünschte Zusatzqualifikationen:

  • Selbständiges und genaues Arbeiten sowie Einsatzfreude und Bereitschaft zur Übernahme von
    Eigenverantwortung
  • Tragfähigkeit
  • Organisationsgeschick
  • Koordinationsfähigkeit und Flexibilität
  • Hands-on Mentalität
  • Gepflegtes und sicheres Auftreten

Wir bieten zahlreiche Benefits, unter anderem:

  • Gute öffentliche Erreichbarkeit mit Zug und Bus
  • Flexible Arbeitszeit
  • Arbeitsmedizinische Leistungen
  • Gesundheitstag/Gesundheitsförderung
  • Universitätssportprogramm
  • Einkaufsvergünstigungen
  • Sprachförderung

Bewerbung und Unterlagen

Referenznummer: 2411APC
Ende der Bewerbungsfrist: 12.12.2024

Personen mit Behinderung oder chronischen Erkrankungen, die, die geforderten Qualifikationskriterien
erfüllen, werden ausdrücklich zur Bewerbung aufgefordert.

Um eine geschlechtsneutrale Formulierung zu gewährleisten, werden geschlechterspezifische Artikel,
Pronomen und Adjektive im Text abgekürzt dargestellt.

Leider können die Reise- und Aufenthaltskosten, die aus Anlass des Aufnahmeverfahrens entstehen,
nicht vergütet werden. Die Aufnahmen erfolgen nach den Bestimmungen des Universitätsgesetzes
2002 (UG) und des Angestelltengesetzes.

Die Montanuniversität Leoben strebt eine Erhöhung des Frauenanteiles an und fordert deshalb
qualifizierte Frauen ausdrücklich zur Bewerbung auf. Frauen werden bei gleicher Qualifikation wie der
bestgeeignete Mitbewerber vorrangig aufgenommen.

Für Ihre Bewerbung verwenden Sie bitte unser Online Bewerbungsformular auf der Homepage:
https://www.unileoben.ac.at/jobs

Dr. Christian Rauch (Deputy Chair)

Elmar Rueckert people_groupleader

Short Bio

Christian is a Senior Scientist at CPS. He received his Ph.D. in “Robotics and Autonomous Systems” from the University of Edinburgh in 2020 under the supervision of Prof. Maurice Fallon and Prof. Timothy Hospedales, and later joined the Statistical Machine Learning and Motor Control Group under supervision of Prof. Sethu Vijayakumar. After a period at the Bosch Center for Artificial Intelligence, he returned to academia in 2025.

His research lies at the intersection of robotics, computer vision, and machine learning, with a focus on enabling interactive manipulation in unknown and unstructured environments.

Research Interests​

Machine learning, Robot Learning, Robot Vision, Perception and Manipulation.

Contact

Dr. Christian Rauch
Research Group Leader at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18, 
8700 Leoben, Austria 

Phone:  +43 3842 402 – 1908
Email: the format of the address is first name dot last name at domain of the university
Chat: https://unileoben.webex.com/meet/christian.rauch

Selected Publications

Richard Marecek

Elmar Rueckert people_alumni

Student Assistant at the Montanuniversität Leoben

MarecekRichard

Short bio: Richard Marecek started at CPS in October  2024.

He is a bachelor student at Montanuniversität Leoben in the program Industrial Data Science. His research interests include industrial process modeling based on large sensor arrays and machine learning algorithms. 

Research Interests

  • Industrial Process Modeling 
  • Machine Learning
  • Python Programming

Contact

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

Email: richard.marecek@stud.unileoben.ac.at

Rino Morina

Elmar Rueckert people_alumni

Student Assistant at the Montanuniversität Leoben

RinoMorina

Short bio: Rino Morina started at CPS in October  2024.

Rino Morina is a bachelor student at Montanuniversität Leoben in the program Industrial Data Science. His research interests include data modeling based on machine learning algorithms including neural networks. 

Research Interests

  • Data Modeling 
  • Machine Learning
  • Python Programming

Contact

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

Email: rino.morina@stud.unileoben.ac.at

Julia Schmelz

Elmar Rueckert people_alumni

Student Assistant at the Montanuniversität Leoben

Portrait_Schmelz

Short bio: Julia Schmelz started at CPS in October  2024.

Julia Schmelz is a master student at Montanuniversität Leoben in the program Industrial Data Science. Her research interests include data modeling based on machine learning algorithms including neural networks. 

Research Interests

  • Data Modeling 
  • Machine Learning
  • Python Programming

Contact

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

Email: julia-maria.schmelz@stud.unileoben.ac.at

Enrollment as a student

Elmar Rueckert wiki_phds

This post is intention to help future international PhD students to ease their enrollment process.

Step 1: Find a mentor and prepare your documents

You need to find a topic, working title, individual curriculum and supervising team (supervisor and mentor) yourself to be able to fill out the form. According to our curriulum for the doctoral studies the mentor needs to have a venia docendi. 

If your degrees are not from a EU country, you need a full authentication or an Apostille of your documents (and the translations, if your documents are not in English), please inform yourself if you’d need either an authentication or Apostille for Austria! This is valid for both, Master’s documents as well as bachelor’s!

If you can not obtain an authentication/Apostille we also accept a translation of your 4 academical documents by an officially licenced translator in Austria instead.

Step 2: Applications form

You have also required to fulfil 20 ECTS, they have to be split in main and secondary subject:

  • Courses that don’t have anything to do with your subjects (language courses etc.) you can do up to 4 ECTS

  • Privatissima (talking to your prof, SE type) for a maximum of 4 ECTS

  • There is no limit on IV courses

  • And you can also have stuff (eg. summer school) from other unis, however you have to go through the equality process (they will check if it really is equal to the ects here)

These stated in the curriculum, which is in German.

Example course:

 

Step 3: Online registration for graduates of Non-Austrian University

Create a basic account in MUonline of the university. You have to create a student account even you have a staff account.

p/s: Sign out your staff account when you’re in the process of creating a basic account.

Detailed admission process at here.

Step 4: Waiting and make appointment to verify the real document

CPS 5-Finger Robot Hand 2024

Elmar Rueckert research_robots

Self-made, dexterous 5-finger hand

We use a adult-sized robot hand for learning grasping and object manipulation skills. The hand is mounted on our FRANKA EMIKA Panda robot. 

The hand has 19 degrees-of-freedom and uses 8 smart actuators for precise control (actuators contained inside the unit).

Under actuated design aims to provide the right balance between fine control and conformance to the shape of the objects.

Webserver GUI for Control

Within a student project, an ESP32 based web-server was developed for controlling the hand, see the git repository. 

Videos

  • Research videos using the robot will be presented here. 
 

Publications

Sorry, no publications matched your criteria.

190.015 Applied Machine and Deep Learning (5SH IL, WS)

Elmar Rueckert teaching_currentlectures

Course Content

In the first week, advanced machine and deep learning methods like multi-layer-perceptrons, convolutional neural networks, variational autoencoder, transformers, simultaneous navigation and mapping approaches, and more will be presented.

These methods can be tested using interactive tools like for example using   https://playground.tensorflow.org. To deepen the knowledge, students will answer well-crafted scientific questions using latex handouts alone or in teams of two students in the lecture room. 

Additionally, Jupyter notebook files were prepared to implement advanced machine and deep learning approaches without installing any software. For all participants of the course user accounts will be created using our JupyterHub at https://jupyter.cps.unileoben.ac.at. The accounts will remain active till the end of the semester. 

Prerequisites & If you Miss Course Contents

During the first week, a laptop or tablet will be needed to use the interactive tools and the Jupyter notebooks. 

When you miss some course contens

If you miss some course contents due to overlapping events, you can watch recordings of the sessions online. All recordings will be hosted via Moodle at https://moodle.unileoben.ac.at/course/view.php?id=3082.

 

Course Description

Modern machine learning methods and in particular deep learning methods are entering almost all areas of engineering. 

The integrated course enables the students to apply these methods in the application domains of their study.

For this purpose, current problems from the industry are investigated and the possibilities of machine and deep learning methods are tested.

Students gain a deep understanding of method implementations, how data must be prepared, which criteria are relevant for selecting learning methods, and how evaluations must be performed in order to interpret the results in a meaningful way.

Initially, the basics of learning methods are developed in 5-6 lecture units. Then, students select one of the listed industrial problems and work on it alone or as a team (with extended assignments). The project work is accompanied by weekly tutorials with tips and tricks. Finally, the project results are discussed in a written report and presented for a final 10-15min.

Grading is based on the quality of the code, the report, and the final short presentation.

Among others, one of the following industry problems can be chosen:

1. Application and comparison of deep neural networks for steel quality prediction in continuous casting plants with data from the ‘Stahl- und Walzwerk Marienhütte GmbH Graz’.

2. Predictive maintenance of bearing shells using frequency analysis in decision trees and deep neural networks based on acoustic measurement data.

3. Motion analysis and path planning for human-machine interaction in logistics tasks with mobile robots of the Chair of CPS.

4. Autonomous navigation and mapping with RGB-D cameras of the four-legged robot Unitree Go1 for excavation inspection in mining.

The project list is continuously extended.

Links and Resources

Location & Time

  • Location: HS 3 Studienzentrum
  • Dates: 13.10.2026 – 13.01.2027, see the course schedule above.
  • Location: Digital Science Center (Roseggerstraße 11, 8700 Leoben)
  • Date: 13.01.2027, 10:15 – 15:15, final presentation
 

Kickoff meeting of project

All meetings will be conducted at CPS chair. The time please refer to the email, contact us if reschedule is needed.

Previous Knowledge Expected

Formal Prerequisite: Basic Python programming skills, Fundamentals of Statistics.

Recommended Prerequisites: Introduction to Machine Learning (“190.012” and “190.013”).

Slides

Learning objectives / qualifications

  • Implement or independently adapt modern machine learning methods and in particular deep learning methods in Python.

  • Analyze data of complex industrial problems, process (filter) the data, and divide it into training- and test data sets such that a meaningful interpretation is possible.
  • Define criteria and metrics to evaluate evaluations and predictions and generate statistics.

  • Develop, evaluate, and discuss meaningful experiments and evaluations.

  • Identify and describe assumptions, problems, and ideas for improvement of practical learning problems.

Grading

Continuous assessment: During the lectures and the tutorials 0-20 bonus points can be collected through active participation.

Project assignments: Alone or in small groups (2-3 students) one of the listed projects has to be implemented. A written report in form of a git repository wiki page have to be submitted.
– For the implementation (Python Code) 0-40 Points can be obtained.
– For the wiki page report, 0-60 Points will be given.

Grading scheme: 0-49,9 Points (5), 50-65,9 Points (4), 66-79 Points (3), 80-91 Points (2), 92-100 Points (1).

With an overall score of up to 79%, an additional oral performance review MAY (!) also be required if the positive performance record is not clear. In this case, you will be informed as soon as the grades are released. If you have already received a grade via MU online, you will not be invited to another oral performance review.

Literature

Machine Learning and Data-modelling:

– Rueckert Elmar 2022. An Introduction to Probabilistic Machine Learning, https://cloud.cps.unileoben.ac.at/index.php/s/iDztK2ByLCLxWZA

– James-A. Goulet 2020. Probabilistic Machine Learning for Civil Engineers. MIT Press.

– Bishop 2006. Pattern Recognition and Machine Learning, Springer.

Learning method Programming in Python:

– Sebastian Raschka, YuxiH. Liu and Vahid Mirjalili 2022. Machine Learning with PyTorch and Scikit- Learn. Packt Publishing Ltd, UK.

– Matthieu Deru and Alassane Ndiaye 2020. Deep Learning mit TensorFlow, Keras und TensorFlow.js., Rheinwerk-verlag, DE. 

Problem specific Literature:

– B. Siciliano, L.Sciavicco 2009. Robotics: Modelling, Planning and Control, Springer.

– Kevin M. Lynch and FrankC. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.

– E.T. Turkogan 1996. Fundamentals of Steelmaking. Maney Publishing,UK.