The SAIL fellowship program is looking for postdocs and advanced researchers who want to continue and expand their research in line with the SAIL research agenda at Universität Bielefeld or Universität Paderborn for a short fellowship (up to 3 months). The program is aimed at enriching the research carried out in SAIL, supporting research ties with relevant communities, and establishing long-term collaboration with institutes across the globe. The fellowships are intended to strengthen the innovation potential of researchers with expertise in the field of AI through further training and interdisciplinary collaboration within the research network.

Neuroexplicit models combine neural and human-interpretable ('explicit') models to overcome the limitations that each model class has separately. They include neurosymbolic models, which combine neural and symbolic models, as well as combinations of neural and physics-based models. The Research Training Group (RTG) aims to improve the state of the art in natural language processing ('Language'), computer vision ('Vision'), and planning and reinforcement learning ('Action'), and to develop novel machine learning techniques for neuroexplicit models ('Foundations'). The goal is to contribute to a better understanding of the cross-cutting design principles of effective neuroexplicit models through interdisciplinary collaboration.

The Research Training Group 2853 “Neuroexplicit Models of Language, Vision, and Action” is looking for 12 PhD Students - Fall 2025. Neuroexplicit models combine neural and human-interpretable (“explicit”) models in order to overcome the limitations that each model class has separately. They include neurosymbolic models, which combine neural and symbolic models, but also e.g. combinations of neural and physics-based models. In the RTG, we will improve the state of the art in natural language processing (“Language”), computer vision (“Vision”), and planning and reinforcement learning (“Action”). We also develop novel machine learning techniques for neuroexplicit models (“Foundations”). Our overarching aim is to contribute to a better understanding of the cross-cutting design principles of effective neuroexplicit models through interdisciplinary collaboration. The RTG is scheduled to grow to a total of 24 PhD students by 2025. An excellent and international group of twelve PhD students and one postdoc have already joined the RTG. Through the inclusion of ~20 associated PhD students and postdocs funded from other sources, it will be one of the largest research centers on neuroexplicit or neurosymbolic models in the world. The RTG brings together researchers at Saarland University, the Max Planck Institute for Informatics, the Max Planck Institute for Software Systems, the CISPA Helmholtz Center for Information Security, and the German Research Center for Artificial Intelligence (DFKI). All of these institutions are collocated on the same campus in Saarbrücken, Germany. The positions will be funded for four years at the TV-L E13 100% pay scale. They are intended to start in September 2025, but could start a little earlier or later depending on the student’s availability.

The Center for Theoretical and Computational Neuroscience (CTCN) at Washington University in St Louis invites applications from outstanding Postdoctoral Fellows to work at the interface between theoretical and experimental neuroscience labs at WashU. The CTCN is a joint initiative between the Schools of Medicine, Engineering, and Arts and Sciences, and provides a hub for neuroscientists to collaborate with mathematicians, physicists and engineers to find creative solutions to some of the most difficult problems currently facing neuroscience and artificial intelligence. Each CTCN Postdoctoral Fellow is based in at least two labs, but also has the opportunity to seek out new collaborations which help build new connections within the WashU community. We are looking for people with drive, independence and outstanding prior achievement, who are committed to leveraging interdisciplinary collaboration to drive forward the field of theoretical and computational neuroscience. Washington University in St Louis is ranked in the top 10 worldwide for Neuroscience and Behavior. Salary for CTCN Fellows is significantly above standard NIH postdoc rates, and funds for conference travel are included. In addition, WashU offers excellent benefits and comprehensive access to career development, professional and personal support. The St Louis metropolitan area has a population of almost 3M and is rich in culture, green spaces and thriving music and arts scenes, with a highly accessible cost of living.

We understand the brain in representational terms. E.g., we understand spatial navigation by appealing to the spatial properties that hippocampal cells represent, and the operations hippocampal circuits perform on those representations (Moser et al., 2008). Philosophers have been concerned with the nature of representation, and recently neuroscientists entered the debate, focusing specifically on neural representations. (Baker & Lansdell, n.d.; Egan, 2019; Piccinini & Shagrir, 2014; Poldrack, 2020; Shagrir, 2001). We want to know what representations are, how to discover them in the brain, and why they matter so much for our understanding of the brain. Those questions are framed in a traditional philosophical way: we start with explanations that use representational notions, and to more deeply understand those explanations we ask, what are representations — what is the definition of representation? What is it for some bit of neural activity to be a representation? I argue that there is an alternative, and much more fruitful, approach. Rather than asking what representations are, we should ask what the use of representational *notions* allows us to do in neuroscience — what thinking in representational terms helps scientists do or explain. I argue that this framing offers more fruitful ground for interdisciplinary collaboration by distinguishing the philosophical concerns that have a place in neuroscience from those that don’t (namely the definitional or metaphysical questions about representation). And I argue for a particular view of representational notions: they allow us to impose the structure of one domain onto another as a model of its causal structue. So, e.g., thinking about the hippocampus as representing spatial properties is a way of taking structures in those spatial properties, and projecting those structures (and algorithms that would implement them) them onto the brain as models of its causal structure.