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April 2026

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BELIEF 2026: extended submission deadline May 31, 2026
by David Mercier April 18, 2026

April 18, 2026

(Our apologies for any duplicate messages) ********************************************************** The 9th International Conference on Belief Functions (BELIEF 2026) https://www.bfasociety.org/Belief2026 Dates: October 19-21, 2026 Location: Nanjing, China * May 31, 2026: Paper submission deadline (extended) * June 15, 2026: Author notification * June 30, 2026: Camera-ready copy due ********************************************************** The Theory of belief functions, also referred to as Evidence theory or Dempster-Shafer theory is a well-established mathematical framework for reasoning and decision-making with uncertainty, with applications to machine learning, statistical inference, information fusion, knowledge representation, risk analysis, etc. It is an extension of probability theory and it has well understood connections with related frameworks such as random sets and imprecise probabilities. Recently, generalised evidence theory, based on random fuzzy sets, has been proposed as a unifying framework encompassing both Dempster-Shafer and possibility theories. The biennial BELIEF conferences (sponsored by the Belief Functions and Applications Society) are dedicated to the confrontation of ideas, the reporting of recent achievements and the presentation of the wide range of applications of this theory. Previous editions of this conference series were held in Brest, France (2010); Compiègne, France (2012); Oxford, UK (2014); Prague, Czech Republic (2016); Compiègne, France (2018); Shanghai, China (2021); Paris, France (2022), and Belfast, UK (2024). To support cross-fertilization among researchers working in different subfields of AI and related disciplines, tutorials and special sessions will be proposed, with emphasis on the links between machine learning, statistical inference and uncertain reasoning, including topics such as quantification of prediction uncertainty, fusion rules for ensemble learning, belief propagation in deep neural networks, links with explainable and symbolic AI, etc. Submissions of papers combining several of these topics, or more generally at the cross-road of belief functions and other AI methods or uncertainty theories, along with relevant applications, are welcome. Proceedings BELIEF 2026 proceedings will be published by Springer-Verlag in the Lecture Notes in Artificial Intelligence (LNCS/LNAI) series and indexed by: ISI Web of Science; EI Engineering Index; ACM Digital Library; dblp; Google Scholar; IO-Port; MathSciNet; Scopus; Zentralblatt MATH. IJAR Special issue Authors of selected papers from the BELIEF 2026 conference will be invited to submit extended versions of their papers for possible inclusion in a special issue of the International Journal of Approximate Reasoning. BELIEF 2026 Conference co-chairs Prof. Zhigang Su, Southeast University, China Prof. Thierry Denoeux, Université de Technologie de Compiègne, France More information: https://www.bfasociety.org/Belief2026

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PhD position at UTC (Compiègne, France)
by Thierry Denoeux April 10, 2026

April 10, 2026

PHD POSITION AT UTC (COMPIÈGNE, FRANCE) PhD topic: Robust ensembling in set-valued classification Application deadline: May 10, 2026 Start date: October 2026 Duration: 3 years Funding: MESR doctoral grant Location: Université de technologie de Compiègne (UTC), Heudiasyc laboratory, Compiègne, France Supervisors: Thierry Denoeux – thierry.denoeux(a)hds.utc.fr Vu-Linh Nguyen – vu-linh.nguyen(a)hds.utc.fr Project summary: This PhD project aims to develop a new framework for robust ensemble learning in set-valued classification, with applications to multiclass, multilabel, and multidimensional classification. The research will combine machine learning, uncertainty quantification, information fusion, and evidence theory, with strong mathematical and algorithmic components. Candidate profile: - Master’s degree or engineering degree in computer science, applied mathematics, or related field - Good programming skills (Python, PyTorch, TensorFlow, etc.) and/or strong background in mathematics - Autonomy, rigor, and critical thinking Application: Please send the following documents to both supervisors: - CV - Motivation letter - Academic transcripts - Contact details of at least two references Keywords: machine learning, uncertainty quantification, information fusion, belief functions, evidence theory, multilabel classification ---------- Prof. Thierry Denoeux Université de technologie de Compiègne Institut universitaire de France Rue Roger Couttolenc, CS 60319 60203 Compiègne cedex, France https://www.hds.utc.fr/~tdenoeux

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