We are seeking highly motivated and talented individuals to join our dynamic research team for combining symbolic and sub-symbolic AI. The successful candidates will conduct research at the Johannes Kepler University in collaboration with our partner institutes AAU Klagenfurt, ISTA, TU Graz, TU Vienna, and WU Vienna. 

Location:

• Linz, Johannes Kepler University

Job description:

The vision of Bilateral AI, öffnet eine externe URL in einem neuen Fenster is to educate a new generation of top-quality AI scientists with a holistic view on symbolic and sub-symbolic AI methods. The training will be distributed over the six participating Universities. Joint seminars, scientific workshops, and compulsory courses outside the PhD students’ research fields, will be also designed to encourage interdisciplinarity. Apart from that, students will be involved in grant applications, conference organization, Bachelor and Master student supervisions, and teaching. Each student will be supervised by two experienced and internationally renowned professors with different research fields (symbolic / sub-symbolic AI). The training will also provide a career development program, advice and support for students with innovative business ideas, and workshops for presentation and soft skills.

Requirements:

• MSc degree in AI, Computer Science, Mathematics, Statistics or related fields 
• background on Machine Learning or Automated Reasoning
• experience with programming in Python or C/C++
• strong written and verbal communication skills
• willingness and ability to work in a team and to support students

What we Offer:

• PhD: On the basis of full-time employment (40 hours/week) the minimum salary in accordance with the collective agreement, CA Job Grade: B1 € 3.277,30 brutto (14 x per year)
• Opportunity to work together with leading experts in the field
  • Ass.-Prof. Johannes Brandstetter, öffnet eine externe URL in einem neuen Fenster
  • Prof. Johannes Fürnkranz
  • Prof. Sepp Hochreiter, öffnet eine externe URL in einem neuen Fenster
  • Prof. Günter Klambauer
  • Ass.-Prof. Erich Kobler
  • Prof. Markus Schedl, öffnet eine externe URL in einem neuen Fenster
  • Prof. Martina Seidl
  • Prof. Gerhard Widmer

• Opportunities for professional development and career advancement
• Stable employer
• Attractive campus environment with good public transportation connections
• State-of-the-art research infrastructure
• Broad range of on-campus dining services/healthy meals (organic food at the cafeteria)
• Exercise and sports classes (USI)
• …and much more

Application Deadline:

Open until filled. Applications will be processed on a regular basis.  

Only full application documents will be considered

Our project is committed to increase the proportion of academic female faculty and, for this reason, especially welcomes applications by qualified women. If applicants are equally qualified, a woman will be given preference for this position.

How to Apply:

If you are interested in a position, please send regular application documents including

1. letter of motivation (detailing previous research achievements, research goals, career plans);
2. a complete CV, including a list of previous scientific expertise, awards, grants, stays abroad, attended lectures, attended summer schools, attended workshops, skills, and publications (if applicable);
3. abstract in English of the applicant’s MSc thesis, BSc thesis or of a research project;
4. a complete list of completed studies and transcripts of all grades;
5. contact details of two reference persons (at least one academic) willing to provide a recommendation letter. The letters should be sent to a dedicated email address (see below).
6. proof of proficiency in English (usually TOEFL/IELTS/CAE);
7. (optional) the selection of one or more research projects and related supervisors.

via email to: recruiting-jku(at)bilateral-ai.net

Furthermore, two reference letters should be sent (there will be no invitation e-mail) with the subject “Candidate name_of_the_candidate” to references(at)bilateral-ai.net within two weeks from submission of the application.

Exemplary Projects (selection):

RM3.2 Computational logic for scalable symbolic machine learning. 

(PI: Johannes Fürnkranz)

In computational logic and automated reasoning several successful and promising highly efficient logic-based optimization techniques have emerged, including the well-developed technologies of SAT, MaxSAT, SMT, constraint optimization, model counting or answer set programming. In this project, we aim at exploring the use of such techniques for the efficient learning of symbolic machine learning models. In particular, we are interested in learning deeply structured rule-based models, in analogy to deep neural networks, which are currently beyond the capabilities of the state-of-the-art in this area.

RM4.2/4.3 Boosting performance of symbolic reasoning engines. 

(PI: Martina Seidl)

Symbolic reasoning engines like SAT solvers are successfully applied to many hard problems in formal verification, planning, game playing, scheduling, etc. In this project, we aim to employ ML techniques to identify paths towards a good pruning of the search space for problems beyond SAT and to identify structural features that enable efficient solving of (sub-)problems. We also want to explore how learning techniques can be exploited for the synthesis of winning strategies, yielding the solutions of certain problems beyond SAT.  

RM1.1: Graph representations: scaling by semantic and visual abstractions.
 

(PI: Günter Klambauer)

The original “Semantic Web” vision has recently evolved into the more general field of “Knowledge Graphs” and remains highly relevant as a driver for taking the next steps in AI and Data Management: a recent roadmap for AI published by the Computing Community Consortium with the support of AAAI stresses the importance of “Open Knowledge Repositories” and platforms as a basis for future AI developments. Such knowledge repositories have emerged in the form of fast growing real-world knowledge graphs (KG)s, i.e., knowledge bases represented as graph structures with a flexible, constantly evolving schema. KGs – partially constructed by automated means from structured data at lower levels of abstraction, or by collaborative editing – form a natural glue between symbolic and subsymbolic representations underlying AI and ML pipelines

RM7.2: Fairness via logic and unlearning biases.

(PI: Markus Schedl)

Societal biases and cognitive biases have been researched extensively in sociology, psychology, and economics. They refer to discrepancies between an ideal world and the real world (societal biases) and to systematic perceptual deviations from rationality in humans' cognitive and neurological processes (cognitive biases). Both kinds of biases can be harmful if they lead to unfair decisions made by AI systems, e.g., in recommender systems adopted in the recruiting domain. Therefore, this PhD thesis aims to uncover different societal and cognitive biases, predominantly in the context of learning to rank (LTR) methods, used in retrieval and recommender systems. Biases should be studied in different components of the ML pipeline, such as high-dimensional numerical input data, learning algorithm and training setup, and model output. Methods to formalize the identified biases related to, and connecting, different components using logical formulas will be researched, leveraging symbolic AI. These formulas will be transformed into easily understandable representations for domain experts and system end-users. Finally, they will be used to mitigate or adjust biases in ML/DL architectures, again focusing on LTR scenarios.