Responsibilities
* Development and implementation of lectures in the area of Machine Learning, Deep Learning and Reinforcement Learning
* Scientific publication activity and participation in scientific conferences
* Collaboration in the preparation of research proposals
* Collaboration in interdisciplinary projects at the institute and preparation of project reports
* Collaboration in the organization of scientific conferences & workshops at the institute
Profile
Admission Requirements
* Doctoral degree in Computer Science, or equivalent fields of study
Desired Qualification
* Publication activity in the field of machine learning and AI.
* Research activity in the field of brain-inspired machine learning.
* Research at the interface of machine learning and symbolic AI.
We Offer
* Interesting area of responsibility
* Collegial and friendly working atmosphere
* International training and teaching opportunities
* Seal of quality for in-house advancement of women
* Award for being the most family-friendly company in Styria 2018
* Subsidy for public transport
* University’s sports program
* Shopping Discounts
* Workplace Health Management
* Top research infrastructure and access to the latest technologies
* Exciting opportunities for professional and personal development
* Safe and stable working environment
* Possibility for home office
We offer an annual gross salary of at least € 69,060.60 for a full-time position. An overpayment based on qualification and experience is possible.
Graz University of Technology aims to increase the proportion of women, in particular in management and academic staff, and therefore qualified female applicants are explicitly encouraged to apply. Preference will be given to women if applicants are equally qualified.
Graz University of Technology actively promotes diversity and equal opportunities. Applicants are not to be discriminated against in personnel selection procedures on the grounds of gender, ethnicity, religion or ideology, age, sexual orientation (Anti-discrimination).
People with disabilities and who have the relevant qualifications are expressly invited to apply.
Organisational Unit
The Institute of Theoretical Computer Science was founded in 1992 to research fundamental problems in information processing, such as the design of computer algorithms, the complexity of computations and computational models, automatic knowledge acquisition (machine learning), the complexity of learning algorithms, pattern recognition with artificial neural networks, computational geometry and information processing in biological neural systems. Its research integrates methods from mathematics, computer science and computational neuroscience. In teaching, the institute is responsible for courses and seminars that introduce students to the basic techniques and results of theoretical computer science. In addition, it offers advanced courses, seminars and applied computing projects in computational geometry, complexity theory, machine learning and neural networks.
About us
Graz University of Technology is the longest-established university of technology in Austria. Here, successful teams of students, talented up-and-coming scientists, ambitious researchers and a lively start-up scene enjoy an inspirational environment as well as access to top-quality equipment. And all this in one of the most innovative and livable regions in Europe. TU Graz offers an inspiring working environment with outstanding infrastructure and service-oriented university management.
Contact
Graz University of Technology
Dean of the Faculty of Computer Science and Biomedical Engineering
For further questions, please contact Robert Legenstein, robert.legenstein@tugraz.at (no applications). Please note that we only accept applications submitted via our online application portal. Applications by e-mail or post will not be considered.
Become part of the team of Graz University of Technology - we are looking forward to your application!
Job details
Title
Graz University of Technology is an important university in the international research and education network of engineering and science.
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