Are you looking for a PhD position in the burgeoning field of neurosymbolic artificial intelligence at a thriving interdisciplinary institute in Amsterdam? Join us in the research unit of Natural Language Processing at the Institute for Logic, Language and Computation (ILLC), a home to a thriving community of scholars, including philosophers, logicians, mathematicians, computer scientists, linguists, musicologists, and cognitive scientists, who share a fascination with the interdisciplinary study of information.

Are you looking for a postdoc position at the intersection of logic, philosophy and semantics? Would you like to be part of a research team developing new foundations for intensional notions such as property, content, proof, possibility and necessity? If so, come and join the GOOD INTENSIONS project!

Are you looking for a postdoc position at the intersection of logic, philosophy and mathematics? Would you like to be part of a research team developing new foundations for intensional notions such as property, content, proof, possibility and necessity? If so, come and join the GOOD INTENSIONS project!

Are you looking for a PhD position at the intersection of logic, philosophy and semantics? Would you like to be part of a research team developing new foundations for intensional notions such as property, content, proof, possibility and necessity? If so, come and join the GOOD INTENSIONS project!

The Humane AI research priority area of the University of Amsterdam is looking for a new PostDoc to amend our existing team (https://humane-ai.nl/). AI is everywhere. Chatbots, translation tools, shopping assistants, hyper-personalised services - as Large Language Models become widely accessible, they're reshaping how we live, work, play and connect. This rapid shift sparks big questions: Whose norms and values shape this AI-driven society—and who gets left behind? What are the rules that govern the development and deployment of Generative AI different sectors of society, and towards what kind of society will they steer us? Is the risked-based approach to AI.

Are you deaf or hard-of-hearing (DHH)and have you studied/worked at the University of Amsterdam, either now or in the past? Would you like to help empower more DHH students to pursue higher education? If you answered "yes" to both, please connect with our researchers at the SignLab: you can either send an email to Jos Ritmeester or fill out the form here. 

The SignLab is located in Bushuis, Kloveniersburgwal 48, Amsterdam. The expexted duration of the chat is 2,5 hours and you will receive a compensation of €37,50 + travel costs.  Thank you for sharing your stories - your experiences, challenges, and successes will help us make a difference. 

 

We are pleased to announce that Martha Lewis has been awarded a a NIAS Lorentz Theme Group - Fellowship, to work on what representations AI models need in order to reason analogically.

An unambiguous problem is a computational search problem for which any instance has at most one solution. We are interested in particular in problems where this uniqueness of solutions is due to the structure of the problem itself, as opposed to being due to a (hard to verify) promise that any solution is unique. There are not many such problems in the class NP (amongst problems that are not known to be polynomial-time solvable), but there are various interesting ones in the class Σ^P_2. For example, some are based on the idea that we can design competitions for which there is at most one winner. With exponentially-many competitors, we seek a competitor that beats all his opponents: if "beats" is checkable in polynomial time, we have a Σ^P_2 problem having at most one solution. Towards classifying the complexity of these problems, we introduce complexity classes "Polynomial Tournament Winner" and "Polynomial Condorcet Winner".

This is joint work in progress with Matan Gilboa, Elias Koutsoupias, and Noam Nisan. I will try to include reminders of definitions of any complexity classes I refer to, apart from P and NP.