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1 PhD Position/4-Year Contract, Dec 15th 2021, RefID: 2111WPH

November 18, 2021February 14, 2022 Elmar Rueckert pastjob

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.

B.Sc. or M.Sc. Thesis/Project: Deep Learning for Predicting Meniscus Level Fluctuations in the Mold at voestalpine Stahl GmbH, Linz

November 10, 2021November 10, 2021 Elmar Rueckert teaching_openthesestopics

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.

B.Sc. or M.Sc. Thesis/Project: Dimensionality Reduction using Variational Autoencoder on Synchrotron XRD data

November 10, 2021November 10, 2021 Elmar Rueckert teaching_openthesestopics

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.

Internship Positions – Just contact us!

November 4, 2021October 21, 2022 Elmar Rueckert job

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.