Connectionists: [Jobs] 2 PhD Positions at U Amsterdam on Learning Causally Grounded Concepts

[Sara Magliacane]

Are you interested in improving the interpretability, robustness and safety
of AI by integrating causal reasoning? The Causality team in the AMLab group
<https://amlab.science.uva.nl/> at the University of Amsterdam is looking
for 2 PhD students to work on this topic in the NWO VIDI sponsored project
CANES (CAusal NEuro-Symbolic approach to integrating perception and
abstract reasoning) led by prof. Sara Magliacane.
<https://saramagliacane.github.io/>
We will develop methods to learn from unstructured data Causally Grounded
Concepts, i.e., concepts with theoretical guarantees as error bounds and
sample complexity, in challenging settings with continuous and discrete
valued concepts (e.g., symbols in neuro-symbolic methods), correlations
between concepts, and when we do not have (all) labels for the concepts,
but we only have a weak supervision signal, e.g., labels of a downstream
task, and background knowledge about the concepts. We also envision some
practical applications of this framework in cross-species translation
(transfer of findings from animal studies to humans) in drug discovery,
dynamical systems for long-horizon time series forecasting, and verifiably
safe reinforcement learning.

While both PhD positions are part of the same project, each of them has an
independent research direction, allowing for a level of autonomy. The first
project (“Discrete concepts”) will focus on developing strong theoretical
guarantees beyond the current work on independent continuous concepts to
allow also for dependent and discrete concepts. The second project (“Weak
supervision”) will instead focus on providing similar guarantees by
integrating background knowledge and weak supervision, e.g. based on labels
in downstream tasks, which can be for example logical constraints.

*Deadline: *20 April 2026

*Details and application link: *
https://www.academictransfer.com/en/jobs/359354/2-phd-positions-on-learning-causally-grounded-concepts-for-safe-ai
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