Please prepare nominations as a single pdf file following the convention LASTNAME_FIRSTNAME.pdf so the file name corresponds to the nominee and then email them to [email protected], provided the file is less than 25MB. For files larger than 25MB, contact the ISBA Prize Committee at [email protected] to make appropriate alternative arrangements.
The Mitchell Prize is given in recognition of an outstanding paper that describes how Bayesian analysis has solved an important applied problem. The prize includes a check for $1000 and a plaque. The winner(s) will be announced at the Joint Statistical Meetings (JSM) in Chicago in August 2027. For details on the Mitchell Prize, including names of past winners, eligibility details, and submission information, please visit https://bayesian.org/
Nominations for the Mitchell Prize may be made by any ISBA or SBSS member. To join ISBA please go to https://bayesian.org/membership/joinrenew/
]]>Please prepare nominations as a single pdf file following the convention LASTNAME_FIRSTNAME.pdf so the file name corresponds to the nominee and then email them to [email protected] provided the file is less than 25MB. For files larger than 25MB, contact the ISBA Prize Committee at [email protected] to make appropriate alternative arrangements.
The Savage Award, named in honor of Leonard J. “Jimmie” Savage, is bestowed each year to two outstanding doctoral dissertations in Bayesian econometrics and statistics, one each in Theory & Methods and Applied Methodology. Up to two awards of $750 will be awarded. Finalists will be notified in mid-December 2026 and invited to present their dissertation research at the Joint Statistical Meetings (JSM) in Chicago in August 2027. The winners will also be announced at the same meeting. For details on the Savage Award, including names of past winners, eligibility details, and submission information, please visit https://bayesian.org/
Nominations for the Savage Award may be made by any ISBA or SBSS member. To join ISBA please go to https://bayesian.org/membership/joinrenew/
]]>Speaker: Janet van Niekerk, The University of Pretoria, South Africa
Title: INLA for Biostatistics
Time: Thursday, March 19, 2026, 11:00 am -12:00 noon (US EST)
Zoom Link: https://us06web.zoom.us/j/89750941157?pwd=EasXS7lmjUTbGTLjaXEyctnWN89srE.1
Meeting ID: 897 5094 1157
Passcode: 946170
Abstract: INLA has been established as an accurate approximate method in Bayesian inference. A modern formulation of INLA combining the Laplace method with Variational Bayes, was proposed and implemented in 2021. This new formulation enables various applications that were previously infeasible. In this talk I will give the intuition behind the modern INLA framework, and present various case studies within the field of Biostatistics where INLA has been recently applied.
]]>—>The deadline for applications is April 14, 2026.
For more info, please visit https://bss2026.lakecomoschool.org/ùù
or contact
[email protected], or sonia.petrone@unibocconi,it
This program brings together a bootcamp, two international workshops, and a symposium, with the objective of strengthening connections between the statistics, artificial intelligence, and data science communities around key challenges such as robustness, uncertainty, trustworthiness, and high-impact applications, featuring experts from the international research community.
➡️ INFORMATION >>> https://ivado.ca/en/thematic-programs/statistical-foundations-of-ai/?menu_1
➡️ REGISTRATION >>> https://event.fourwaves.com/thematicsemester-statisticalai-ivado/registration
The proposed activities are as follows:
🔹Bootcamp : Statistical Insights into Modern AI Systems May 4–8, 2026
🔹Workshop 1 : Statistics in Trustworthy AI May 11–15, 2026
🔹Workshop 2 : Uncertainty in AI June 8–11, 2026
🔹Symposium : AI meets statistics: biomedical data perspectives August 20–21, 2026
These activities are primarily intended for graduate students, postdoctoral fellows, and researchers in statistics, biostatistics, AI/ML, and data science.
We would be delighted if these events could be promoted through your immediate network to encourage broad participation and foster new scientific collaborations.
We sincerely thank you for your help in promoting these activities.
p.p. Alex Schmidt [for Aurélie Labbe (HEC Montreal)] ]]>
The successful applicant will carry out independent research and supervision activities in the field of
Statistics (with a focus on Statistical Computing).
For the official job announcement and more information, please see https://wirtschaftsuniversitaet-wien-portal.rexx-systems.com/Assistant-Professor-tenure-track-qualification-agreement-eng-j2682.html, where you can also submit your application by March 11, 2026 (ID 2682).
]]>Bayesian Structural Learning (BSL) refers to methods that uncover the hidden structure of complex stochastic systems while explicitly quantifying uncertainty. This is essential in modern data science: it allows us to reason about competing structural explanations, assess their implications, and make robust decisions under uncertainty.
BSL captures both:
1. the core statistical activity of modeling and understanding structural aspects of high‑dimensional and complex data, and
2. the Bayesian principles that enable coherent uncertainty quantification, principled model comparison, and transparent integration of prior knowledge.
We believe that these ideas are currently spread across multiple ISBA Sections; but not explicitly represented by any of them; motivating the need for a dedicated BSL Section.
The new BSL Section aims to provide an inclusive home for researchers working on methods for high‑dimensional or structurally complex data, including:
* flexible regression models (latent variable & hierarchical approaches)
* dependence modeling (graphical models, copulas, networks)
* causal inference
* spatio-temporal processes
* factor models
* clustering and mixture modeling
If you support this initiative, please consider sharing this post with colleagues who might be interested.
Thank you very much for your time and support.
Latin American Regional Section of the International Association for Statistical Computing (IASC-LARS ISI)
Mexican Association of Statistics Asociación Mexicana de Estadística (AME)
International Environmetrics Society (TIES)
Section on Environmental Sciences of the International Society for Bayesian Analysis (EnviBayes ISBA)
Section on Statistics and the Environment of the American Statistical Association (ENVR ASA).
Theme/Vision: CLIMATE CHANGE IN 2026
We envision a future where diverse fields of knowledge converge to address environmental challenges with innovative, holistic, and sustainable solutions. Through collaboration across environmental statistics, data science, and computing disciplines – we strive to foster a deep understanding of ecological systems, human impact, equitable, and explainable solutions. By bridging expertise and perspectives, we empower communities, institutions, and industries to create a resilient, sustainable, thriving planet for future generations.
Event goals:
To provide an opportunity for sharing knowledge, collective learning, and collaboration between researchers and practitioners in academia, government, industry, for addressing current and future problems, issues, and policies in environmental statistics, data science, and computing.
Encourage membership recruitment, volunteer participation of students and early-career individuals in committees, current/future activities of the organizing societies.
Website:
]]>World-class resources for research incl. LUMI supercomputer, dozens of supervisors across Finnish universities, and close collaboration with companies and the ELLIS network.
Apply by February 9: https://www.ellisinstitute.fi/postdoc-recruit-2026
]]>The selected applicant will be expected to deliver excellent teaching in support of both the Data Science and Operations Research MS/PhD programs. Likewise, the selected candidate will be expected to supervise graduate research at the MS/PhD level. Finally, the selected applicant will need to engage/support external DoD and US government stakeholders.
AFIT’s Data Science faculty are housed within the Department of Operational Sciences. The department’s research spans a broad array of topics in operations research and data science. Its student body is primarily composed of military officers from US and allied nations (e.g., Chile, Turkey, Israel, Brazil), along with DoD civilians and contractors. The department has close connections with myriad USG and DoD stakeholders as well.
The salary range is broad (i.e., $75,469 to – $209,600 per year) and is determined by the applicant’s credentials. AFIT employees are eligible for a pension akin to other USG employees, along with other federal benefits.
Interested faculty may apply at https://www.usajobs.gov/job/854185600
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