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PhD and Postdoc opportunities at University of Oslo
by Giulia Di Nunno 03 Feb '26

03 Feb '26

Dear All, Exciting opportunities are open within the group of Risk and Stochastics at the Department of Mathematics, University of Oslo Up to 2 PhD positions in stochastic analysis and computational mathematics. Connection to SURE-AI community Read here: https://www.jobbnorge.no/en/available-jobs/job/294743/phd-research-fellow-i… One Postdoc position in stochastic analysis for research lovers in a simmering group. Read here: https://www.jobbnorge.no/en/available-jobs/job/294745/postdoctoral-research… Deadline for application March 1st, 2026! Please help us distribute this information widely. Best regards, Giulia Di Nunno Professor Risk and Stochastics Department of Mathematics University of Oslo, Norway

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Special Issue ANOR - Climate Transition and Operational Risk Modelling: Implications for Supply Chains and Financial Decision-Making
by Andrea Flori 03 Feb '26

03 Feb '26

Dear Colleagues, We are pleased to announce that the Call for Papers for the Special Issue of Annals of Operations Research, entitled “Climate Transition and Operational Risk Modelling: Implications for Supply Chains and Financial Decision-Making”, has been published. We particularly welcome applied and methodological operations research studies that address these challenges in an innovative and rigorous manner. Topics of interest include, but are not limited to: - Robust stochastic optimization methods for climate transition risks - Portfolio optimization and risk-adjusted return modelling under climate policy uncertainty - Credit and counterparty risk assessment under low-carbon transition scenarios - Risk-sharing mechanisms and insurance models for climate-related disruptions - Supply chain risk management in climate transition - Operational decision-making in emission trading schemes and carbon pricing - Predictive modelling of carbon stranding risk via supervised learning - Machine learning and big data analytics for climate risk scenario classification - Natural language processing applications in climate policy risk analysis - Bayesian network approaches to modelling climate transition risk propagation - Agent-based models of climate transition dynamics and systemic effects. Submission deadline: December 31, 2026 Further details: https://link.springer.com/collections/baifaedejd Guest Editors Duc Khuong Nguyen, EMLV Business School, France & University of Cambridge, UK Andrea Flori, Politecnico di Milano, Italy Anna Maria Gambaro, University of Eastern Piedmont, Italy Ioannis Kyriakou, University of London, UK Best Regards, Andrea Flori

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Bando RTT (STAT/04) a Venezia Ca' Foscari
by Marco LI CALZI 03 Feb '26

03 Feb '26

Care Colleghe, Cari Colleghi, è stato pubblicato oggi 3 febbraio il bando per una posizione RTT in STAT/04 presso il Dipartimento di Management dell’Università Ca’ Foscari Venezia. Il bando resterà aperto per 60 giorni. Vi chiediamo la cortesia di divulgare la notizia verso i potenziali interessati. Qui trovate tutte le informazioni utili: https://www.unive.it/data/11306/?id=2026-UNVE000-0018567 Grazie per l’attenzione, marco licalzi. __________________________________ Marco Li Calzi Venice School of Management Università Ca' Foscari Venezia San Giobbe, Cannaregio 873 30121 Venezia, Italy T. +39 041 234 6925 M. licalzi(a)unive.it https://mlicalzi.github.io

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Fwd: CMI-HIMR Summer School on Random Geometries and Random Matrices (Bristol, June 2026)
by Anna Paola Todino 03 Feb '26

03 Feb '26

Dear Colleagues, I would like to inform you about the *CMI-HIMR Summer School on Random Geometries and Random Matrices*, which will be held in Bristol from June 1 to 5, 2026. Further information and registration details are available at: https://web-eur.cvent.com/event/a35d84e8-2f74-41c1-a550-c8bc3b9fea9a/summary Partial coverage of local expenses may be available. PhD students are especially welcome to register. Please note that the registration deadline is *February 8*, 2026 (23:59 UK time). Best regards, Anna Paola Todino

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SLMath SGS-1126 Mathematics for Machine Learning
by Domenico Marinucci 03 Feb '26

03 Feb '26

Care colleghe e cari colleghi segnalo la scuola estiva di Mathematics for Machine Learning, che si terrà all'ICTP di Trieste dal 15 al 26 giugno 2026, sito web: https://indico.ictp.it/event/11148 Alcuni dettagli qui sotto: -------------------------------------------------------------------------------------------- Lecturers: Francesca Mignacco (Princeton University) Andrea Montanari (Stanford University) Course by Andrea Montanari This summer school provides an introduction to theoretical ideas that have been developed with the objective of understanding machine learning methods and their domain of applicability. The focus will be on proof technique and general mathematical tools. 1.Empirical risk minimization and empirical process theory. Objective: Understand how learning is formalized in terms of generalization guarantees, and how the latter emerge from uniform control over suitably restricted function classes. a.Uniform convergence guarantees for learning b.Complexity bounds for neural networks c.Tools: Strong laws of large numbers, concentration of measure, Radamacher complexity. 2.Generalization in the linear regime. Objective: Understand how and when good generalization can be achieved while violating the (naive) assumptions of uniform convergence. In particular, characterize the generalization error of kernel and random features methods. a.Interpolation and benign overfitting b.Kernel methods c.Random features and neural tangent models d.Tools: Random matrix theory 3.Feature learning in large networks. Objective: Analysis of simple neural network models (two-layer network) outside the linear/neural tangent regime. a.Mean field theory b.Multi-index models c.Tools: Propagation of chaos, Wasserstein gradient flows. 4.Sampling and generative methods. Objective: Describe and justify mathematically architecture for generative models. Presents correctness guarantees, as well as limits in some simple settings. a.Autoregressive models, diffusion models, probability flows. b.Sampling guarantees under assumptions on the score estimation. c.Impossibility results. d.Tools: Stochastic localization, functional inequalities, computation-information gaps. Course by Francesca Mignacco The lectures will present an overview on the statistical physics approach to high-dimensional learning problems. The main concepts and methods from mean-field theory of disordered systems will be introduced through prototypical examples that are amenable to analytic characterization. More precisely, the course syllabus will be as follows: 1. Motivation and background. Objective: Introduce the statistical physics perspective on high-dimensional learning problems and the main concepts and settings covered throughout the course. (a) The jargon of statistical physics: thermodynamic limit, order parameters, typical-case scenario. (b) The perceptron. (c) The teacher-student paradigm. 2. Statics of learning. Objective: Understand the Bayes-learning framework and how to apply it to study the impact of data structure and architectural bias on the performance. (a) Generalization error in the perceptron: Bayes-optimal performance vs empirical risk minimization. Introduction to the replica method. (b) Memory capacity: from random points to neural manifolds. Applications to deep learning and neuroscience. (c) Models of data structure. (d) Deep linear networks and back-propagating kernel renormalization 3. Dynamics of learning. Objective: Understand how to derive effective low-dimensional descriptions of the learning dynamics and apply them to study commonly-used training algorithms such as stochastic gradient descent. (a) Online learning in two-layer neural networks. (b) Batch learning: dynamical mean-field theory, cavity method and path integral formulation. Participants: The school can be of interest for a very broad range of students basically from all areas of Pure and Applied Mathematics. Indeed, on one hand the topics of the school are specially addressed to those that are working in disciplines directly involved with Machine Learning (such as mathematical statistics, probability, information theory, numerical analysis, statistical mechanics); on the other hand, the lectures are developed starting from a limited requested background in these specific disciplines, so that students with good mathematical foundations in other areas could attend as well. Hence this school could provide the occasion for students with a different background to get into an exciting new area of research, either to find new connections at the theoretical level or to gain further skills which are on extremely high-demand at every level. The school may also be suitable for students who are not enrolled in a mathematical PhD but are working in related areas, such as statistics, computer science, engineering. Organizing committee: Claudio Arezzo (ICTP), Jean Barbier (ICTP), Filippo Bracci (Roma Tor Vergata and INdAM), Domenico Marinucci (Rome Tor Vergata and INdAM), Cristina Trombetti (Napoli Federico II and INdAM), ---------------------------------------------------------------------------------------------------------------- Grazie per l'attenzione, Domenico Marinucci -- @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Domenico Marinucci Dipartimento di Matematica Università di Roma Tor Vergata https://sites.google.com/view/domenicomarinucci @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

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LSAA 2026, June 15–18, now open for registration
by Andreas Petersson 03 Feb '26

03 Feb '26

Dear colleagues (apologies if you get multiple copies of this email), Registration is now open for the Linnaeus Workshop on Stochastic Analysis and Applications (LSAA 2026), held at Linnaeus University in Växjö, Sweden, during the Midsummer week of June 15–18, 2026. We would be grateful if you could forward this information within your department. LSAA is an annual conference series (since 2010) and a meeting point for researchers working on stochastic processes, differential equations, and applications, with a focus on fostering collaboration across stochastics, numerics, analysis, and complex systems. We welcome contributed talks, and PhD students and early-stage researchers are especially encouraged to participate. The workshop is held in conjunction with the 3rd Young Summer School on Stochastic Analysis. Register and find details: https://lnu.se/en/lsaa2026 We look forward to welcoming you to Växjö in June! Best regards, Andreas Petersson, Wolfgang Bock, and Astrid Hilbert (Workshop organizers)

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IPMU 2026 - Deadline extension for paper submission
by Davide Petturiti 02 Feb '26

02 Feb '26

****** PLEASE APOLOGIZE FOR MULTIPLE COPIES ****** ********************************************************** 21st International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2026) June 15th-19th, 2026, Rome, Italy https://www.sbai.uniroma1.it/conferenze/ipmu2026/ *********************************************************** Due to numerous requests, the deadline for paper submission at IPMU 2026 has been extended to February 15, 2026, both for long and short papers. This will be the final, hard deadline. Papers must be submitted in PDF via EasyChair at https://easychair.org/my/conference?conf=ipmu2026 Submission instructions can be found at https://www.sbai.uniroma1.it/conferenze/ipmu2026/submission-instructions.php IMPORTANT DATES: Paper submission deadline: February 15, 2026 Notification of acceptance: March 15, 2026 Camera-ready versions: April 1, 2026 Conference: June 15-19, 2026 Contact: ipmu2026(a)sbai.uniroma1.it KEYNOTE SPEAKERS: We are also pleased to inform about the keynote speakers at the conference: Bob Kowalski (Imperial College London, United Kingdom) Luc de Raedt (KU Leuven, Belgium) María Ángeles Gil (University of Oviedo, Spain) Marco Zaffalon (IDSIA, Switzerland) GRANTS AND AWARDS: EUSFLAT is providing 5 student grants to support student participation at the conference. Eligibility criteria and instructions can be found at https://www.sbai.uniroma1.it/conferenze/ipmu2026/student-grants.php In addition, there shall be a number of awards for the best paper and best student paper at the conference. General Chair Barbara Vantaggi (Italy)

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Postdoc position at Warwick
by Francesco Caravenna 02 Feb '26

02 Feb '26

Ricevo e inoltro. > Da: "Zygouras, Nikolaos" <N.Zygouras(a)warwick.ac.uk> > Oggetto: postdoc position > Data: 1 febbraio 2026 alle ore 14:49:48 CET > A: Francesco Caravenna <francesco.caravenna(a)unimib.it> > > Dear Francesco, > > I would like to bring to your attention a postdoc position that I will be having > > https://warwick-careers.tal.net/vx/appcentre-ext/brand-4/spa-1/candidate/so… > > I would be grateful if you could forward this announcement to interested parties. > I would also be happy to have an informal discussion, with anyone interested. > > many thanks, Nikos

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Opening of PhD position within Emmy-Noether group at MPI MiS Leipzig
by Theresa LANGE 02 Feb '26

02 Feb '26

Dear colleagues, With this I would like to make you aware of the opening of a PhD position on stochastic fluid dynamics within my Emmy-Noether group <https://www.mis.mpg.de/noise-in-convection> at the Max-Planck-Institute of Mathematics in the Sciences in Leipzig, Germany (duration: 3 years; earliest possible start: May 2026). All details on the position and how to apply can be found under the following link: https://www.mathjobs.org/jobs/list/28040. May I ask you to advertise this position among suitable and interested candidates as well as among further colleagues? Thank you very much! Kind regards Theresa Lange — Dr. Theresa Lange Postdoctoral Researcher Group Prof. Franco Flandoli Scuola Normale Superiore Piazza dei Cavalieri 7 56126 Pisa Italy

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SPASS seminar
by Francesco Grotto 02 Feb '26

02 Feb '26

Dear Colleagues, We would like to invite you to the following SPASS seminar, *Current trends and challenges in atmospheric CO2 monitoring* by Carina Geldhauser (ETH) on Tuesday 3.2.2026 at 14:00 CET in Aula Riunioni, Dipartimento di Matematica, Pisa. Best, F. Grotto on behalf of the organizers Abstract: In this era of rapid climate change, the precise monitoring of carbon dioxide emissions has become a critical task. We review current efforts to model the spatial propagation of CO2 concentration levels. We highlight the limitations of current results from isolated megacities for European setting: In Europe, the spatiotemporal distribution of CO2 is heavily influenced by atmospheric dynamics and terrestrial biospheric fluxes, hindering a succesful application of traditional modelling approaches. Hence, we prototyped the application of machine learning models to predict ground-level CO2 from satellite measurements. Our results show the effectiveness of integrating diverse data sources in capturing local emission patterns, highlighting the value of high-resolution atmospheric transport models.

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