PhD Position (m/f/d) in Machine Learning and Computational Neuroscience, Group Leader Robert Legenst

Responsibilities

• Teaching in the fields of machine learning and computational neuroscience. Independent teaching of compulsory courses in these areas for the degree programs Computer Science, Biomedical Engineering, Artificial Intelligence Engineering, as well as courses from the Machine Learning elective catalog for Master's programs in the above-mentioned fields of study, among others
• Supervision of projects, bachelor's theses and master's theses
• Research in the fields of machine learning and computational neuroscience
• Publication of research results in relevant journals and at conferences
• Writing a subject-specific dissertation
• Managing and updating software for simulations
• Assisting with the management of projects and administration at the institute

Admission Requirements

Completed master's or diploma degree in Computer Science, or equivalent fields of study

• Very good academic record
• Very good knowledge in the fields of machine learning and computational neuroscience

Desired Qualification

• Experience in the simulation of spiking neural networks
• Good knowledge of English
• Knowledge of German desirable
• Very good communication skills and the ability to work carefully in a scientific environment
• Experience in assisting in university teaching is desirable (e.g. tutoring, supporting activities).
• The willingness and ability to teach courses in the fields of machine learning and computational neuroscience is expected.

We Offer

We offer an annual gross salary of at least € 52,007.20 for a fulltime 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.

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.

Organisational Unit

The Institute of Machine Learning and Neural Computation 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.

Contact

Graz University of Technology
Dean of the Faculty of Computer Science and Biomedical Engineering

For further questions, please contact Robert Legenstein, robert.legenstein at 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.