• conduct independent research in the field,
• collaborate in machine learning and deep learning projects,
• publish in renowned international journals and conferences.

• Master’s degree,
• track record in machine learning (e.g., deep learning, reinforcement learning, kernel methods, probabilistic modeling, meta-learning, attention models),
• knowledge in one or more of the following application domains is a plus: signal processing, vision, speech, natural language processing, physics, bio-/chemoinformatics, computational medicine, autonomous driving,
• willingness and ability to work in a team.

Within the last years, Sepp Hochreiter (who is best known for the invention of LSTM, for the Vanishing Gradient problem, “Flat Minima”, and “Learning to Learn”) has built up a dynamic team of more than 20 researchers.

The group has recently achieved widely acclaimed contributions and successes, such as winning the NIH’s Tox21 toxicity prediction challenge with deep learning, the invention of the ELU activation function, Self-Normalizing Networks, and a convergence proof for GAN learning.

The group has many international collaborations and receives funding from national and international research programs as well as from companies, such as Johnson & Johnson, Merck, Bayer, Zalando, and from joined labs like the Audi.JKU Deep Learning Center.

The area offers an excellent quality of living in the heart of Europe – close to the alps between Vienna, Salzburg, Prague and Munich.

Linz provides a superb cultural environment, most famous for the Ars Electronica Festival, the Brucknerfest, and the nearby Salzburg Festival.

The picturesque and versatile landscape provides countless options for recreation and sports in nature (skiing, hiking, climbing, cycling, and many more).

Application documents (in English or German) must include:

• Diplomas issued by a post-secondary education institution, including transcripts and an explanation of the used grading system

• Master's thesis abstract

• Curriculum vitae

• Letter of Intent (with preference for an advisor or topic)

• Letter(s) of recommendation

• Suggestions for potential PhD supervisors or potential research directions (optional)

• About Linz, opens an external URL in a new window
• Information for Researchers from Outside of Austria, opens an external URL in a new window
• Austrian Wage Agreement for Universities (in English), opens an external URL in a new window
• Kollektivvertrag für Universitäten (in German), opens an external URL in a new window