1 Secretary – July 1st 2021, RefID: 2106APC

1 Stelle für eine/n halbbeschäftigte/n Sekretär/in am Department Product Engineering – Lehrstuhl Cyber
Physical Systems an der Montanuniversität Leoben ab ehest möglichem Termin in einem unbefristeten
Arbeitsverhältnis Verw.Gr. IIb nach Uni-KV, monatl. Mindestentgelt exkl. Szlg.: 2.023,50 € für 40 Wochenstunden (14xjährlich), tatsächliche Einstufung erfolgt lt. anrechenbarer tätigkeitsspezifischer Vorerfahrung).

Aufgabenbereich

Korrespondenz; Buchhaltungs- und Verrechnungsaufgaben; Tätigkeiten im Zusammenhang mit MU_online und PURE betreffend den Lehrstuhl; Studentenbetreuung; Parteienverkehr; Allgemeine Büro- und Verwaltungstätigkeiten; Büromaterialverwaltung, eigenverantwortliche Führung der Lehrstuhlbibliothek.

Voraussetzungen

Abgeschlossene kaufmännische Ausbildung (Handelsschule oder ähnliches).

Erwünschte Qualifikation

Ausgezeichnete Korrespondenz in deutscher und englischer Sprache; sehr gute EDV-Kenntnisse (MS Office); SAP-Kenntnisse; MUonline-Kenntnisse; Kenntnisse im Bereich der Buchhaltung und des Rechnungswesens, Homepage-Kenntnisse, Selbständiges und genaues Arbeitensowie Einsatzfreude und Organisationsgeschick.

Männliche Bewerber nur nach abgeschlossenem Präsenz-/Zivildienst.

Bewerbung und Unterlagen

Application deadline: June 30th, 2021

Online Bewerbung auf: Montanuniversität Leoben Homepage (unter dem Kürzel 2106APC)

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.

1 PhD Position – October 1st 2021, DFG-Train-Project

1 position for a fully employed University Assistant at the Chair of Cyber-Physical-Systems on the Department of Product Engineering starting at 1st of October in an initial 1-year term of employment with the option of extension by another three years. 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.

Job Description

Within the research project TRAIN we are looking for a highly motivated student with experience in one of the fields robotics, reinforcement learning, machine learning or computational neuroscience.
The student will investigate novel transfer learning strategies of robot manipulation tasks that can be learned from human demonstrations and corrections. The methods include probabilistic deep learning, stochastic neural networks and probabilisitic inference approaches. The target evaluation plattform is a compliant robot arm FRANKA EMIKA equipped with a five finger hand.

The position provides the possibility of gaining a PhD degree.

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.

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. Willingness and ability for scientific work in research including publications with the possibility to write a dissertation.

Desired additional qualifications

Programming experience in one of the languages C, C++, C#, JAVA, Matlab, Python or similar is beneficial. Experience with Linux or ROS is advantageous. Good English skills and willingness to travel for research and to give technical presentations.

Application & Materials

A complete application includes a (1) detailed curriculum vitae, (2) a letter of motivation, (3) two letters of recommendation, (4) the master’s thesis as PDF file or link, (5) all relevant certificates of prior education for bachelor’s and master’s studies, (6) name, email and phone number of two additional references to contact, (7) previous publications as PDFs or links (e.g. from M.Sc. studies) are not required but will be considered in favor of the candidate.

Application deadline: Open until the position is filled.

Online Application via: Details to the application process will be published soon.

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.

1 Lab Technician – June 30th 2021, RefID: 2105APD

1 position for a fully employed Lab Technician at the Chair of Cyber-Physical-Systems on the Department of Product Engineering at the earliest possible date or beginning on 15th of June. in a 1-year term of employment with the option of extension in a permanent position. Salary Group 3A to Uni-KV, monthly minimum charge excl. SZ.: € 2.147,30 for 40 hours per week (14 times a year).

Job Description

The tasks include setting up PCs, installing and maintaining operating systems, maintaining GitHub repositories and our webpage and content management systems. Installing robotic hardware (e.g., FRANKA Emika robot arms), recording and editing robotic videos, working with the modern robotic and machine learning tools.

What we offer

Being part of a young research team, helping creating a new lab environment. Versatile tasks including working with modern or self-build robotic and machine learning systems. Full time position with the option of an open-ended contract.

Requirements

Completed technical high school with an university entrance qualification or equivalent education (in german: HTL Absolvent mit Maturaabschluß).
We require self-motivation, independence, problem solving oriented thinking, technical interests in computer science topics, reliability and sociability or the ability to work in a team.

Application & Materials

Details to the application process will be published soon.

Application deadline: June 30th, 2021

Online Application via: Montanuniversität Leoben Webpage (search for 2105APD)

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.

1 Senior Researcher / Research Group Leader (Postdoc) – June 30th 2021, RefID: 2103WPX

1 position for a fully employed Senior Scientist at the Chair of Cyber-Physical-Systems on the Department of Product Engineering at the earliest possible date or beginning on 15th of June in a 1-year term of employment with the option of extension in a permanent position. Salary Group B1 to Uni-KV, monthly minimum charge excl. SZ.: € 3.945,90 for 40 hours per week (14 times a year).

Job Description

The group’s research topics are autonomous systems, machine and deep learning, embedded smart sensing systems and computational models. The senior researcher will work on one of these topics (or combinations of them), where a focus will be developed jointly based on the experience of the candidate. The researcher will be further engaged in teaching (e.g., student supervision), project management and funding applications.

What we offer

We offer a research position in fascinating fields with the opportunity to develop own ideas and implement them independently. Further, the researcher is part of a young and newly formed team, learns and assumes leadership responsibilities with coaching sessions, and receives targeted career guidance for a successful scientific career.

Requirements

Degree in computer science, physics, telematics, electrical engineering, mechanics, robotics or mathematics with a PhD. Experience in at least one of the topics of machine learning, neural networks, robot learning or learning sensor systems. Willingness and ability to co-supervise scientific work in research including related publication activities. Programming experience in one of the languages C, C++, C#, JAVA, Matlab, Python or similar.

Desired additional qualifications

Scientific experience demonstrated by patents and publications in international conferences and journals on machine learning, neural networks, robotics or sensing. Experience in obtaining external funding and in collaborations with industrial partners. Good English skills and willingness to travel for research and to give technical presentations. Ability to work in a team, sociability, self-motivation and reliability.

Application & Materials

A complete application includes a (1) detailed curriculum vitae, (2) a letter of motivation with a reference to the desired field of research and teaching from the above-mentioned topics, (3) two letters of recommendation, (4) the PhD thesis as PDF file, (5) all relevant certificates of prior education for bachelor’s, master’s and PhD studies, (6) name, email and phone number of two additional references to contact, (7) previously published or submitted publications as links or PDF files.

Application deadline: June 30th, 2021

Online Application via: Montanuniversität Leoben Webpage

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.

3 PhD Positions – June 30th 2021, RefID: 2103WPW

3 positions for fully employed University Assistant’s at the Chair of Cyber-Physical-Systems on the Department of Product Engineering at the earliest possible date or beginning on 15th of June in a 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.

The following doctoral theses are available:

Fundamentals of learning methods for autonomous systems.

The goal is to make autonomous learning systems such as industrial robot arms, humanoid or mobile robots suitable for everyday use. To achieve this, large amounts of data must be processed in a few milliseconds (Big Data for Control) and efficient learning methods must be developed. In addition, safe human-machine interaction must be ensured when dealing with the autonomous systems. For this purpose, novel stochastic motion learning methods and model representations for compliant humanoid robots will be developed.

Fundamentals of stochastic neural networks.

Modern deep neural networks can process large amounts of data and calculate complex predictions. These methods are also increasingly used in autonomous systems. A major challenge here is to integrate measurement and model uncertainties in the calculations and predictions. For this purpose, novel neural networks will be developed that are based on stochastic computations that enrich predictions with uncertainty estimations. The neural networks will be used in learning tasks with robotic arms.

Robot learning with embedded systems.

Modern robot systems are equipped with complex sensors and actuators. However, they lack the necessary control and learning methods to solve versatile tasks. The goal of this thesis is to develop novel AI-based sensor systems and to integrate them into autonomous systems. The developed algorithms will be applied in mobile computers and tested on realistic industrial applications with robotic arms.

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. Willingness and ability for scientific work in research including publications with the possibility to write a dissertation.

Desired additional qualifications

Programming experience in one of the languages C, C++, C#, JAVA, Matlab, Python or similar. Experience with Linux or ROS is advantageous. Good English skills and willingness to travel for research and to give technical presentations.

Application

Application deadline: June 30th, 2021

Online Application via: Montanuniversität Leoben Webpage

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.

How to build a professional low-cost lightboard for teaching

Giving virtual lectures can be exciting. Inspired by numerous blog posts of colleagues all over the world (e.g., [1], [2], [3]), I decided to turned an ordinary glass desk into a light board. The total costs were less than 100 EUR.
Below you can see some snapshots of the individual steps.

Details to the lightboard construction

The light board construction is based on

  • A glas pane, 8mm thick. Hint: do not use acrylic glass or glas panes thinner than 8mm. I got an used glass/metal desk for 20EUR.
  • LED stripes from YUNBO 4mm width, e.g. from [4] for 13EUR. Hint: Larger LED strips, which you can typically get at DIY markets have width of 10mm. These strips do not fit into the transparent u profile.
  • Glass clamps for 8mm glass, e.g., from onpira-sales [5] for 12EUR.
  • Transparent U profiles from a DIY store, e.g., the 4005011040225 from HORNBACH [6] for 14EUR.
  • 4 castor wheels with breaks, e.g. from HORNBACH no. 4002350510587 for 21EUR.

Details to the markers, the background and the lighting

Some remarks are given below on the background, the lighting and the markers.

  • I got well suited flourescent markers, e.g., from [6] for 12EUR. Hint: Compared to liquid chalk, these markers do not produce any noise during the writing and are far more visible.
  • The background blind is of major importance. I used an old white roller blind from [7] and turned it into a black blind using 0.5l of black paint. Hint: In the future, I will use a larger blind with a width of 3m. A larger background blind is required to build larger lightboards (mine is 140x70mm). Additionally, the distance between the glass pane and the blind could be increased (in my current setting I have a distance of 55cm).
  • Lighting is important to illuminate the presenter. I currently use two small LED spots. However, in the future I will use professional LED studio panels with blinds, e.g. [8]. Hint: The blinds are important to prevent illuminating the black background.
  • The LED stripes run at 12Volts. However, my old glass pane had many scratches, which become fully visible at the maximum power. To avoid these distracting effects, I found an optimal setting with 8Volts worked best for my old glass pane.

Details to the software and to the microphone

At the University of Luebeck, we are using the CISCO’s tool WEBEX for our virtual lectures. The tool is suboptimal for interactive lightboard lectures, however, with some additional tools, I converged to a working solution.

  • Camera streaming app, e.g., EPOCCAM for the iphones or IRIUN for android phones. Hint: the smartphone is mounted on a tripod using a smartphone mount.
  • On the client side, a driver software is required. Details can be found when running the smartphone app.
  • On my mac, I am running the app Quick Camera to get a real time view of the recording. The viewer is shown in a screen mounted to the ceiling. Hint: The screen has to be placed such that no reflections are shown in the recordings.
  • In the WEBEX application, I select the IRIUN (virtual) webcam as source and share the screen with the quick camera viewer app.
  • To ensure an undamped audio signal, I am using a lavalier microphone like that one [9].
  • For offline recordings, apple’s quicktime does a decent job. Video and audio sources can be selected correctly. Hint: I also tested VLC, however, the lag of 2-3 seconds was perceived suboptimal by the students (a workaround with proper command line arguments was not tested).

An example lecture

Sicheres Autonomes Fahren mit Probabilistischen Neuronalen Netzen

Wir Menschen sind in der Lage unter widrigen Bedingungen z.B. bei eingeschränkter Sicht, oder bei Störungen komplexe Vorgänge wahrzunehmen, vorherzusagen und innerhalb von wenigen Millisekunden zusammenhängende Entscheidungen zu treffen. Mit dem zunehmenden Grad der Automatisierung steigen auch die Anforderungen an künstliche Systeme. Immer komplexere und größere Datenmengen müssen verarbeitet werden um autonome Entscheidungen zu treffen. Mit gängigen KI Ansätzen stoßen wir aufgrund der konvergierenden Miniaturisierung an Grenzen, die z.B. im Bereich des autonomen Fahrens nicht ausreichen, um ein sicheres autonomes System zu entwickeln.

Ziel dieser Forschung ist es probabilistische Vorhersagemodelle in massiv parallelisierbaren neuronalen Netzen zu implementieren und mit diesen komplexe Entscheidungen Aufgrund erlernter interner Vorhersagemodelle zu treffen. Die neuronalen Modelle verarbeiten hoch dimensionale Daten moderner und innovativer taktiler und visueller Sensoren. Wir testen die neuronalen Vorhersage und Entscheidungsmodelle in humanoiden Roboteranwendungen in dynamischen Umgebungen.

Unser Ansatz beruht auf der Theorie der probabilistischen Informationsverarbeitung in neuronalen Netzen und unterscheidet sich somit grundlegend von den gängigen Methoden tiefer neuronaler Netze. Die zugrundeliegende Theorie ermöglicht weitreichende Modelleinblicke und erlaubt neben den Vorhersagen von Mittelwerten auch Unsicherheiten und Korrelationen. Diese zusätzlichen Vorhersagen sind entscheidend für verlässliche, erklärbare und robuste künstliche Systeme und sind eines der größten offenen Probleme in der künstlichen Intelligenz Forschung.

Dieses Projekt wurde mit dem Deutschen KI-Nachwuchspreis der Bilanz Deutschland Wirtschaftsmagazin GmbH geehrt und demonstriert die Wichtigkeit für Grundlagenforschung in der künstlichen Intelligenz.

H2020 Goal-Robots 11/2016-10/2020

This project aims to develop a new paradigm to build open-ended learning robots called `Goal-based Open ended Autonomous Learning’ (GOAL). GOAL rests upon two key insights. First, to exhibit an autonomous open-ended learning process, robots should be able to self-generate goals, and hence tasks to practice. Second, new learning algorithms can leverage self-generated goals to dramatically accelerate skill learning. The new paradigm will allow robots to acquire a large repertoire of flexible skills in conditions unforeseeable at design time with little human intervention, and then to exploit these skills to efficiently solve new user-defined tasks with no/little additional learning.

Link: http://www.goal-robots.eu

How to build and use a low-cost sensor glove

This post discusses how to develop a low cost sensor glove with tactile feedback using flex sensors and small vibration motors. MATLAB and JAVA code is linked.

Documentation

  • Weber, Paul; Rueckert, Elmar; Calandra, Roberto; Peters, Jan; Beckerle, Philipp
    A Low-cost Sensor Glove with Vibrotactile Feedback and Multiple Finger Joint and Hand Motion Sensing for Human-Robot Interaction Inproceedings
    Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2016. https://ai-lab.science/wp/ROMANS2016Weber.pdf
  • Rueckert, Elmar; Lioutikov, Rudolf; Calandra, Roberto; Schmidt, Marius; Beckerle, Philipp; Peters, Jan
    Low-cost Sensor Glove with Force Feedback for Learning from Demonstrations using Probabilistic Trajectory Representations
    Inproceedings ICRA 2015 Workshop on Tactile and force sensing for autonomous compliant intelligent robots2015https://ai-lab.science/wp/ICRA2015Rueckertb.pdf

Hardware

  • Arduino Mega 2560 Board
  • Check which USB device is used (e.g., by running dmesg). On most of our machines it is /dev/ttyACM0
  • Enable read/write permissions if necessary, e.g., run sudo chmod o+rw /dev/ttyACM0
  • Serial protocoll based communication: Flex sensor readings are streamed and Vibration motor PWM values can be set between 0 and 255
  • Firmware can be found here (follow the instructions in the README.txt to compile and upload the firmware)
  • Features frame rates of up to 350Hz
  • Five flex sensors provide continuous readings within the range [0, 1024]

Simple Matlab Serial Interface – max 100Hz

  • Download the Matlab demo code from here
  • Tell Matlab which serial ports to use: copy the java.opts file to your Matlab bin folder, e.g., to /usr/local/MATLAB/R2012a/bin/glnxa64/
  • Run FastComTest.m

Fast Mex-file based Matlab Interface – max 350Hz

  • Install libserial-dev
  • Download the code from here
  • Compile the mex function with: mex SensorGloveInterface.cpp -lserial
  • Run EventBasedSensorGloveDemo.m

How to build a low-cost USB controlled treadmill

This post discusses how to develop a low cost treadmill with a closed-loop feedback controller for reinforcement learning experiments. MATLAB and JAVA code is linked.

Hardware – Treadmill

  • Get a standard household treadmill Samples
  • Note: It should work with a DC-Motor, otherwise a different controller is needed!

 Hardware – Controller

  • Pololu Jrk 21v3 USB Motor Controller with Feedback or stronger (max. 28V, 3A)
  • Comes with a Windows Gui to specify the control gains
  • Sharp distance sensor GP2Y0A21, 10 cm – 80 cm or similar
  • USB cable
  • Cable for the distance sensor
  • Power cables for the treadmill
  • User Guide: https://www.pololu.com/docs/pdf/0J38/jrk_motor_controller.pdf

 Matlab Interface (max. 50 Hz)

  • Get the java library  build or the developer version, both from Sept 2015 created by E. Rueckert.
  • Run the install script installFTSensor.m (which add the jar to your classpath.txt)
  • Check the testFTSensor.m script which builds on the wrapper class MatlabFTCL5040Sensor (you need to add this file to your path)