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Contact Helga Winklmayr, see our post on responsibilities.

Arbeitssicherheit

• Univ.-Prof. Dr. Elmar Rückert is responsiple for complying with the regulations.
• All new employees get a safety briefing.

XMAS Vouchers for CPS Members

• Every year, the chair can buy vouchers for the CPS members.
• Note that the university also makes gifts to all non-academic in form of vouchers.

General Process

• Wait till the university distributes the vouchers to all non-academic staff members.
• The financial department usually informs about the max. possible amount and how much the university distributes (2021: 186,-max. 30,- uni).
• Fill out the Table in our cloud folder ‘Beschaffung/Weihnachtsfeier’ Q5.1_100_2_CPS_2021.xlsx (last column, vouchers).
• Call the Leoben City Center.
• Send an Email to the Leoben City Center (office@citymanagement-leoben.at) with the order details.
• Get the vouchers at the LCS 2nd floor.
• Send the invoice and the spreadsheet to the financial department.

Email Template

Sehr geehrte Damen und Herren,

 bitte bereiten sie uns Gutscheine in folgender Stückelung vor: 3×180€

1×150€

 Die Rechnungsanschrift lautet: Montanuniversität LeobenLehrstuhl für Cyber-Physical-Systems
Franz-Josef-Straße 18, 
8700 Leoben, Austria 
UID-Nummer: ATU57480504 Vielen Dank und Schöne Grüße, 

Elmar Rückert 

1a SAP GUI for new M1 Macs

Just run the Installer M1_MAC_GUI770Installation_4-70004682.DMG. It will install all dependencies including the Java JDK.

Continue with Step 2.

1b For Old Macs with the Intel CPUs

• Download the openjdk-17.0.1_macos-x64_bin.tar.gz
• Or any other Version from https://jdk.java.net/17/
• Below are instructions for the installation on bigSur
• Execute the following Code in a terminal:

 sudo mv openjdk-17.0.1_macos-x64_bin.tar.gz /Library/Java/JavaVirtualMachines/cd /Library/Java/JavaVirtualMachines/sudo tar -xzf openjdk-17.0.1_macos-x64_bin.tar.gzsudo rm openjdk-17.0.1_macos-x64_bin.tar.gz

Next, check where your JDK is located via:

$ /usr/libexec/java_home -v17This should result in:/Library/Java/JavaVirtualMachines/jdk-17.0.1.jdk/Contents/Home

Add the Java path to the globals:

echo -n “export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk-17.0.1.jdk/Contents/Home” >> ~/.bash_profile

Reload the globals with

source ~/.bash_profile

Test your java installation with
java -version

which schould give something like

openjdk version “17.0.1” 2021-10-19OpenJDK Runtime Environment (build 17.0.1+12-39)OpenJDK 64-Bit Server VM (build 17.0.1+12-39, mixed mode, sharing)

Install the SAPGui

• Download the SAPGui including the MUL connection profile.
• Modify the file extension of ‘PlatinGUI750_12-80002494.JAR’ to be lower-case, i.e., ‘PlatinGUI750_12-80002494.jar‘
• Right Click on the PlatinGUI750_12-80002494.jar file and select Open-With Jar Launcher, see the screenshot below.

Follow the instructions to install the gui.

2 Setup the SAP Server Connection

• Press the Create a New Connection Buttion in SAP GUI
• Open the ‘advanced’ tab and select the ‘expert mode’.
• Enter the connection string: conn=/M/unipu1.ccc.brz.gv.at/S/3620/G/UNI01
• Add a description on top and save the connection!
• Connect once using this connection!
• Afterwards you can use the following connection string to avoid entering the user and client manually: conn=/R/MS/G/UNI01&clnt=110&user=G_CPS&lang=DE&systemName=MS.

3 Install CISCOs ANYCONNECT Client

The SAP software works only within the university network.

Install the VPN Client provided by the university.

1 position for a fully employed University Assistant at the Chair of Cyber-Physical-Systems at the earliest possible date or beginning on 1^st of January 2022 in a full-time (100%) 4-year term of employment. Salary Group B1 to Uni-KV, monthly minimum charge excl. SZ.: € 2.971,50 for 40 hours per week (14 times a year), actual classification takes place according to accountable activity-specific previous experience.

We are looking for a motivated student interested in a PhD thesis on “Robot Learning”. The goal of the doctoral thesis is to develop deep neural networks for interactive learning of autonomous systems and industrial processes. This will involve processing complex sensor data such as from thermal/RGB-D cameras and tactile data for decision making in “greybox” approaches. These hybrid approaches combine the effectiveness of deep neural networks for processing complex data with predictions from analytic dynamic models. The developed models will be tested using realistic industrial applications for process modeling and with robotic systems.

What we offer

The opportunity to work on research ideas of exciting modern topics in artificial intelligence and robotics, to develop your own ideas, to be part of a young and newly formed team, to go on international research trips, and to receive targeted career guidance for a successful scientific career.

Job requirements

Completed master’s degree in computer science, physics, telematics, statistics, mathematics, electrical engineering, mechanics, robotics or an equivalent education in the sense of the desired qualification; Programming experience in one of the languages C, C++, C#, JAVA, Matlab, Python or similar; Willingness and ability for scientific work in research including publications with the possibility to write a dissertation.

Desired additional qualifications

Experience with neural networks, machine learning methods or reinforcement learning. Basic knowledge of Linux or ROS is advantageous. Good English skills and willingness to travel for research and to give technical presentations.

Application

Application deadline: December 15th, 2021

Online Application via: Montanuniversität Leoben Webpage (2111WPH)

The Montanuniversitaet 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.

Supervisors: Univ.-Prof. Dr Elmar Rückert,
Assoc. Prof. Dr. Susanne Michelic & Assoc. Prof. Dr. Christian Bernhard (Chair of Ferrous Metallurgy),
Markus Brummayer (voestalpine Stahl GmbH)
Start date: ASAP from December 2021

Theoretical difficulty: mid
Practical difficulty: mid

Abstract

In this thesis, the student has the unique opportunity to investigate meniscus level fluctuations in the mold using deep learning approaches at the voestalpine Stahl GmbH in Linz. 

The mold, illustrated in the image above, is equipped with electromagnetic mold level sensors and with temperature image cameras that measure the surface temperature of the casting powder. 

The goal of this thesis is to understand and model the underlying dynamic processes of the meniscus level fluctuations in the mold.

In the thesis black box models as well as gray box models that combine analytic dynamic models with learned  models will be investigated. 

Tentative Work Plan

To achieve our aim, the following concrete tasks will be focused on:

• Literature research on meniscus level fluctuations in the mold.
• Data analysis, filtering, preprocessing, visualization of meniscus level fluctuations data. 
• Implementation of  deep convolutional neural networks (CNN) as low-dimensional feature extractors. Visualization and analysis of the dynamic processes.
• (Optional) Implementation of neural time-series models like LSTMs trained with the computed CNN features.
• Analysis and evaluation of the provided data.

Supervisor: Univ.-Prof. Dr Elmar Rückert, Dr. Petra Spörk-Erdely (Chair of Physical Metallurgy and Metallic Materials)
Start date: ASAP from December 2021

Theoretical difficulty: mid
Practical difficulty: low

Abstract

In the context of this thesis, we propose to apply modern machine learning approaches such as variational autoencoder to visualize and reduce the complexity of X-ray diffraction (XRD) data collected on advanced γ-TiAl based alloys. By classifying XRD data collected during in situ experiments into known phases, we aim at disclosing phase transformation temperatures and selected properties of the individual phases, which are of interest with regard to the current alloy development. Furthermore, the capabilities of the applied machine learning approaches going beyond basic XRD data analysis will be explored.

Sketch of a synchron from synchrotron.org.au, illustrating the process of accelerating electrons at almost the speed of light.   

Illustration of a collected data sample which is a 2D X-ray diffraction of a nominal Ti-44Al-7Mo (in at.%) alloy collected.

Topic and Motivation

Intermetallic γ-TiAl based alloys are a promising class of structural materials for lightweight high-temperature applications. Following intensive research activities, they have recently entered service in the automotive and aircraft engine industries, e.g. as low-pressure turbine blades in environmentally-friendly combustion engine options [1].

During the past decades, the development of these complex multi-phase alloys has been strongly driven by the application of diffraction and scattering techniques [2]. These characterization techniques offer access to the atomic structure of materials and provide insights into a variety of microstructural parameters. High-energy X-rays, such as available at modern synchrotron radiation sources (i.e. large-scale research facilities for X-ray experiments), and recent advances in hardware technology nowadays allow to conduct so-called in situ experiments that reveal at a high temporal resolution the relationship between selected external conditions (e.g. thermal or mechanical load) and structural changes in the material. Various specimen environments can be adjusted to emulate technologically relevant or real-life conditions, addressing a multitude of research topics ranging from fundamental questions in the primary alloy design over process-related to application-related issues. Modern setups at synchrotron sources even allow the investigation of elaborate manufacturing, joining and repair processes in an in situ manner, producing insights that have been inaccessible by means of conventional methods so far.

Advanced materials characterization techniques such as described above are often characterized by an ever-growing data acquisition speed and storage capabilities. While enabling novel insights, they, thus, also pose a serious challenge to modern materials science. In situ synchrotron X-ray diffraction (XRD) experiments usually bring about large sets of two-dimensional diffraction data such as those shown in Figure 1. New procedures are needed to quickly assess and analyze this type of datasets.

Tentative Work Plan

To achieve our aim, the following concrete tasks will be focused on:

• Literature research on state-of-the-art materials characterization methods.
• Implementation of  deep convolutional neural networks (CNN) and Variational Autoencoder in Python/Tensorflow.
• Application and evaluation of variational autoencoder on the CNN features.
• Analysis and Evaluation of the provided synchrotron data.

References

[1] Clemens, S. Mayer, Design, processing, microstructure, properties, and applications of advanced intermetallic TiAl alloys, Advanced Engineering Materials 15 (2013) 191-215, doi: 10.1002/adem.201200231.

[2] Spoerk-Erdely, P. Staron, J. Liu, N. Kashaev, A. Stark, K. Hauschildt, E. Maawad, S. Mayer, H. Clemens, Exploring structural changes, manufacturing, joining, and repair of intermetallic γ-TiAl-based alloys: Recent progress enabled by in situ synchrotron X-ray techniques, Advanced Engineering Materials (2020) 2000947, doi: 10.1002/adem.202000947.

Please contact us via cps@unileoben.ac.at if you want to join us for an internship.

We support you when you apply for an internship grant. Below we list some relevant grant application details.

CEEPUS grant (European for undergrads and graduates)

Find details on the Central European Exchange Program for University Studies program at https://grants.at/en/ or at https://www.ceepus.info.

Country of origin

Albania; Bosnia and Herzegovina; Bulgaria; Croatia; Czech Republic; Hungary; Kosovo; Moldova – Republic of; Montenegro; North Macedonia; Poland; Romania; Serbia; Slovakia; Slovenia

Duration

1 to 10 months. Undergraduates need to stay at least 3 months. 

Grant benefit paid

For students and graduates without PhD EUR 1050 per month. For teachers with a PhD at most EUR 1150.

Application Deadlines and Online Application Link

Apply online at https://ceepus.info/

In principle, you can apply at any time for a scholarship. However, also your country of origin matters and there exist networks of several countries that have their own contingent. 

Ernst Mach Grant (Worldwide for PhDs and Seniors)

Find details on the program at https://grants.at/en/ or at https://oead.at/en/to-austria/grants-and-scholarships/ernst-mach-grant.

Country of origin

Worldwide.

Duration

1 to 9 months.

Grant benefit paid

For PhD students EUR 1050 and for senior researcher EUR 1150 per month. 

Application Deadlines and Online Application Link

Apply online at http://www.scholarships.at/

Max. age is 35 years. Forthcoming closing date for applicants is 01.02.2022. However, note that due to the pandemic applications may be processed at any time. 

News on UNI Leoben website: Artificial intelligence as a new research area

This article provides an insight into Univ.-Prof. Rückert’s thoughts on Artificial intelligence, Robots, CPS future and more. 

Brief, but interesting reading can be accessed here.

Feiertage in Österreich / Holidays in Austria

Here is a quick informative list of important Holidays. 
For other information of such nature, visit the main website of our University in Leoben.

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

• Video file.

Publications

2021

Denz, R.; Demirci, R.; Cansev, E.; Bliek, A.; Beckerle, P.; Rueckert, E.; Rottmann, N. A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning Proceedings Article In: International Conference on Advanced Robotics , pp. 7, 2021. Links | BibTeX @inproceedings{Denz2021, title = {A high-accuracy, low-budget Sensor Glove for Trajectory Model Learning}, author = {R. Denz and R. Demirci and E. Cansev and A. Bliek and P. Beckerle and E. Rueckert and N. Rottmann}, url = {https://cps.unileoben.ac.at/wp/ICAR2021Denz.pdf, Article File}, year = {2021}, date = {2021-12-06}, booktitle = {International Conference on Advanced Robotics }, pages = {7}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Close Article File Close