Topic: Machine-learning

Annual conference of the Bernstein Network Computational Neuroscience, bringing together students, postdocs and PIs from around the world to meet and discuss new scientific discoveries in computational neuroscience. Satellite workshops Sep 28-29, main conference Sep 29-Oct 1 at Goethe University, Campus Westend, Frankfurt am Main.

Oden Institute Seminar by Rex Ying (Yale University) on how non-Euclidean geometries, particularly hyperbolic geometry, can enhance foundation models by better capturing hierarchies and symmetries in real-world data, with applications across Transformers, language model training, multimodal systems, and recommender systems.

The 2026 Conference on Empirical Methods in Natural Language Processing, organized by ACL's SIGDAT, held October 24-29, 2026 in Budapest, Hungary, with a main conference plus workshops and tutorials.

The Fortieth Annual Conference on Neural Information Processing Systems brings together interdisciplinary machine-learning research through peer-reviewed sessions, invited talks, demonstrations, tutorials, workshops, and an exposition.

The Fortieth Annual Conference on Neural Information Processing Systems, organized by the Neural Information Processing Systems Foundation, with the main meeting in Sydney, Australia (Dec 6-12) and satellite venues in Atlanta, Georgia and Paris, France (Dec 9-13).

The 41st Annual AAAI Conference on Artificial Intelligence at the Palais des Congres de Montreal, featuring technical papers, special tracks, invited speakers, workshops, tutorials, poster sessions, and competitions to promote AI research and scientific exchange.

Bates College is recruiting two full-time, open-rank computer science faculty members in Digital and Computational Studies, starting in July 2027. Areas of interest include artificial intelligence, machine learning, natural language processing and human-computer interaction, alongside other computer science specialties. Faculty will sustain research, mentor undergraduates and teach within an interdisciplinary liberal arts setting; the annual teaching load is five courses. The advertised salary range is USD 87,000–135,000. Review begins October 15, 2026 and continues until the positions are fil

Conduct research in computational mathematics, including numerical methods for differential and integral equations, machine learning, optimisation, probability, statistics and computational biophysics. Fellows contribute algorithms, scientific computing and open-source tools in collaboration with the centre’s researchers. Appointments initially last two years, renewable for a third, starting between July and October 2027. Annual salary is USD 95,000. This is a New York-based position. Review continues until filled; submit all materials, including letters, by 15 November 2026 for full considera

ISTA Lecture by Klaus Robert Müller (TU Berlin & Korea University, Seoul) on how machine learning and AI enable scientific research, particularly in medicine and chemistry, and on explainability techniques for extracting understanding from machine learning models.

The 19th European Conference on Computer Vision, the biennial premier research conference in computer vision and machine learning managed by the European Computer Vision Association, held at Malmo Arena and Malmomassan in Malmo, Sweden.

Convergence Seminar by Alexis Battle, PhD (Wu and Zhang Professor of Biomedical Engineering and Computer Science, Johns Hopkins University), on computational and machine-learning approaches for analyzing how genetic variation impacts gene regulation and disease, including noncoding sequences and rare variants.

Collaborate with interdisciplinary teams to develop infrastructure for materials science experiments and apply AI/machine learning to accelerate discovery of new functional materials through computational simulation and automated experimentation.

Postdoctoral research advancing generative AI applied to biology, including developing frontier biological foundation models; requires a PhD in computer science or related fields and expertise in Python and deep learning frameworks.

NSF support for interdisciplinary collaborations developing mathematical and theoretical foundations for explainable, reliable, sustainable and trustworthy artificial intelligence.

The astro[sound]bites team introduces cohost Vivasvaan and his work on galaxy mergers and machine learning. The discussion explains why mergers are useful probes of galaxy evolution and gives an early-career researcher’s perspective on the field.

Interviews with Seshadri Nadathur and Maggie Lieu cover cosmological tensions, DESI observations, galaxy clusters and machine learning. Reports from the Euclid-UK and LSST:UK meetings add perspectives on major surveys and the future of observational cosmology.

Predictive processing offers a unifying view of neural computation, proposing that brains continuously anticipate sensory input and update internal models based on prediction errors. In this talk, I will present converging evidence for the computational mechanisms underlying this framework across human neuroscience and deep neural networks. I will begin with recent work showing that large-scale distributed prediction-error encoding in the human brain directly predicts how sensory representations reorganize through predictive learning. I will then turn to PredNet, a popular predictive coding in

This talk aims to answer these questions along four dimensions. First is history. RL was the basis of AI long before the term AI was introduced in 1956. The first machine learning (ML) systems were based on RL even before digital computers existed. Despite notable early successes of ML based on RL, RL essentially disappeared from ML until relatively recently. A second reason for renewed interest in RL is the clarification of some misunderstandings that have been prevalent in the ML community. A third, and most important, reason for this resurgence is that new, or rediscovered, algorithms and

Digital platforms generate unprecedented traces of human behaviour, offering new methodological approaches to understanding collective action, polarisation, and social dynamics. Through analysis of millions of digital traces across multiple studies, we demonstrate how online behaviours predict offline action: Brexit-related tribal discourse responds to real-world events, machine learning models achieve 80% accuracy in predicting real-world protest attendance from digital signals, and social validation through "likes" emerges as a key driver of mobilization. Extending this approach to conspirac

We have entered a golden age of artificial intelligence research, driven mainly by the advances in the artificial neural networks over the last several decades. Applications of these techniques—to machine vision, speech recognition, autonomous vehicles, natural language, and many other domains—are coming so quickly that many observers predict that the long-elusive goal of “Artificial General Intelligence” (AGI) is within our grasp. However, we still cannot build a machine capable of building a nest, stalking prey, or loading a dishwasher. I will describe how evolution may have shaped the algor

There are over 30 million people with drug-resistant epilepsy worldwide. When neuroimaging and non-invasive neural recordings fail to localise seizure onset zones (SOZ), intracranial recordings become the best chance for localisation and seizure-freedom in those patients. However, intracranial neural activities remain hard to visually discriminate across recording channels, which limits the success of intracranial visual investigations. In this presentation, I present methods which quantify intracranial neural time series and combine them with explainable machine learning algorithms to localis

Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery (eg, Wang et al. 2023). Much of this work is motivated by the thought that DL methods might facilitate the efficient discovery of phenomena, hypotheses, or even models or theories more efficiently than traditional, theory-driven approaches to discovery. This talk considers some of the more specific obstacles to automated, DL-driven discovery in frontier science, focusing on gravitational-wave astrophysics (GWA) as a

Developing scientifically-grounded theories for representation learning and generalization in artificial deep neural networks remains a grand challenge of fundamental interest to theoretical neuroscience and machine learning. I will discuss our work on one facet of this challenge — namely understanding generalization or “scaling laws” in learned neural networks as a function of basic control variables. I’ll discuss a taxonomy we develop that classifies different regimes of scaling behavior. We identify regimes where generalization exhibits universal scaling behavior and others where it can be