ERC-Funded PhD and Postdoc positions in Artificial Intelligence and Meta-Learning (deadline February 1st)

[Sebastian Risi]

The positions are funded as part of the GROW-AI project, a 5-year European Research Council (ERC) Consolidator Grant awarded to Sebastian Risi. The PhD positions are full-time positions for 3 years (starting from an MSc or equivalent) or 4 years (starting from a BSc). The postdoc positions are full-time positions funded for an initial period of 2 years, with potential extensions for up to a total of 4 years. 

The project: Growing Machines Capable of Rapid Learning in Unknown Environments

Despite major advances in the field of artificial intelligence, especially in the field of neural networks, these systems still pale in comparison to even simple biological intelligence. Current machine learning systems take many trials to learn, lack common-sense, and often fail even if the environment only changes slightly. In stark contrast to current neural networks, whose architectures are designed by human experts and whose large number of parameters are optimized directly, evolution does not operate directly on the parameters of biological nervous systems. Instead, these nervous systems are grown and self-organize through a much smaller genetic program that produces rich behavioral capabilities right from birth and the ability to rapidly learn. Neuroscience suggests this “genomic bottleneck” is an important regularizing constraint, allowing animals to generalize to new situations. The goal in GROW-AI is to learn such genomic bottleneck algorithms, and in addition, co-optimize task generators that provide the agents with the most effective learning environments. Taking inspiration from the fields of artificial life, neurobiology, and machine learning, we will investigate if algorithmic growth is needed to understand and create intelligence.

The candidate should have experience in some of the following topics:

• Deep RL / meta-learning / continual learning
• Neuroevolution / evolutionary algorithms
• Indirect encodings such as Hypernetworks
• Neurobiology / systems neuroscience / neurogenetics
• Evolutionary developmental biology
• Procedural content generation

Research environment
The candidates will work together with Sebastian Risi (sebastianrisi.com) and join the Creative AI (game.itu.dk/groups/creativeai/) and Robotics, Evolution, and Art Lab (REAL) (real.itu.dk) at the IT University of Copenhagen (itu.dk). Our aim is to build a diverse team. All applications are welcome, especially those from members of underrepresented groups are encouraged.

Application

The application should be in English and should include:

• A cover letter.
• A research statement, which provides evidence of independent thinking, novelty and originality, and a state-of-the-art grasp of the targeted research field matching the project description above.
• A full CV, including name, address, phone number, e-mail, previous and present employment and academic background
• Documentation of academic degrees (copy of degree certificates etc.)
• Applications may also include up to 3 relevant scientific publications written by the applicant, or the applicant’s master thesis. Letters of recommendation can also be included in the application

Applications without the above-mentioned required documents will not be assessed.

General information
The IT University of Copenhagen (ITU) is a teaching and research-based tertiary institution concerned with information technology (IT) and the opportunities it offers. The IT University has more than 160 full-time Faculty members. Research and teaching in information technology span all academic activities which involve computers including computer science, information and media sciences, humanities and social sciences, business impact and the commercialization of IT.

Questions about the positions can be directed to Sebastian Risi, IT University of Copenhagen, e-mail se...@itu.dk. Questions related to the application procedure may be directed to HR, h...@itu.dk.

Link to apply: https://candidate.hr-manager.net/ApplicationInit.aspx?cid=119&ProjectId=181512&DepartmentId=3508&MediaId=5

Deadline: February 1st, 2023