Workplace Description

FBK is a private research institution devoted to excellence in research in numerous disciplines and designated to the role of keeping the Autonomous Province of Trento in the mainstream of European and international research. Each research area is assigned to a specific research center, of which there are twelve totals. Information regarding the research centers, their activities and production is available at http://www.fbk.eu/research-centers. 

The FBK Center for Digital Industry focuses its research on digital technologies for the various domains in industry (e.g., aerospace, railway, automotive, energy, agriculture, manufacturing, etc.) by creating applications for critical systems, adaptive and autonomous systems, advanced perception, diagnostics and prediction systems. It is involved in multiple European research projects and collaborates with numerous institutions and multinational companies on strategic projects with a high innovation content – including the European Space Agency (ESA) and the Italian Railways Network (RFI). 

The position is opened by the Planning Scheduling and Optimization Unit (PSO Unit) of the Digital Industry Center of FBK. The Unit carries out basic and applied research, tool development and technology transfer in the field of automated planning, discrete optimization and process scheduling with applications in autonomous robotic systems and flexible manufacturing. 

FBK actively seeks diversity and inclusion in the workplace and is also committed to promoting gender equality.
To promote the inclusion of disabled staff as per law 68/99, the Foundation is available and interested in evaluating the applications received for technical-scientific domains that do not correspond exactly to this call. 

Job Description 

The open research position is in the field of reinforcement learning for automated planning and scheduling. The STEP-RL project (https://pso.fbk.eu/articles/step-rl), funded by the ERC, aims at using modern reinforcement learning techniques to synthesize guidance of various forms (e.g. heuristics, pruning rules, macro actions, parameters configurations) for state-of-the-art automated temporal planners.

We are looking for exceptional post-doctoral researchers, passionate about the integration of learning and symbolic AI methods: the STEP-RL project will allow the selected candidate to perform full-time curiosity-driven research on the STEP-RL project themes. This is a unique opportunity to pursue basic research in the area of learning for symbolic systems, as the only expected outcomes are top-tier publications and impactful research in the planning and learning communities.

The candidate is expected to work in close collaboration with the project PI (Andrea Micheli), as well as other researchers, programmers and students involved in the unit. Moreover, the candidate is expected to help in the supervision of PhD students involved in the ERC project.