Today's Editor:
Alex Townsend
Cornell University
townsend@cornell.edu
Today's Topics:
Announcing the release of MOLE v1.2
New Book, Perturbation Methods Using Backward Error
MFEM Community Workshop Sep 22-25, 2026
Workshop on Optimization with Differential Equations, Wuerzburg, Sept 16-18 2026
PhD position in Scientific Machine Learning (SciML) in FEMTO-ST, France
PhD Position (3 years) in Applied Mathematics – University of Oulu, Finland
PhD Position: Error Propagation and Implicit Priors, DTU, Denmark
5 PhD Positions in High Performance Scientific Computing – University of Pisa
PhD and Postdoc position in Mathematics at Strathclyde University
Postdoc position in Numerical Linear Algebra at University of Leicester
Assistant Professor in High-performance Computing at Trinity College Dublin
Contents, AIMS New Article: ACSE Vol. 8, Art. 3
Contents, AIMS New Articles: CAC Vol. 9, Art. 1, 3
Contents, AIMS New Article: FAM Vol. 1, Art. 4
Contents, AIMS New Article: MFC Vol. 13, Art. 1
Call for papers, Springer Künstliche Intelligenz special issue AI4Science
Call for papers: Special Issue of the Journal Numerical Algorithms on the occasion of the 65th anniversary of Nicola Mastronardi
See this issue of NA Digest on the web at:
https://na-digest.coecis.cornell.edu/na-digest-html/26/v26n20.html
Submissions, FAQs, and archives:
https://na-digest.coecis.cornell.edu/
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From: Jose E Castillo jcastillo@sdsu.edu
Date: May 08, 2026
Subject: Announcing the release of MOLE v1.2
We are thrilled to announce a new release of The MOLE 1.2 library. The MOLE
Open-source Ecosystem implements high-order mimetic operators in different
programming languages. MOLE provides discrete analogs of the most common
vector calculus operators: Divergence, Gradient, Curl, and Laplacian. These
operators act on functions discretized over staggered grids (uniform,
nonuniform, and curvilinear), and they satisfy local and global conservation
laws. MOLE's operators can be used to develop computationally efficient
programs for solving linear and nonlinear partial differential equations (PDEs)
with higher orders of accuracy than other methods. Visit the MOLE website at
https://mole-ose.org. For MOLE library documentation visit:
mole-docs.readthedocs.io/. To report any issues, please create a GitHub Issue
on the MOLE library repository https://github.com/csrc-sdsu/mole
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From: Mitch Graham mgraham@siam.org
Date: May 12, 2026
Subject: New Book, Perturbation Methods Using Backward Error
Perturbation Methods Using Backward Error by Robert M. Corless and Nicolas
Fillion
"Corless and Fillion have written the rare book that bridges mathematical
practice, computation, and philosophy. Perturbation Methods Using Backward
Error brings new unity and rigor to a venerable subject, showing how backward
error analysis can illuminate every corner of approximation — from asymptotics
and differential equations to computer algebra and modern numerical
methods. Clear, concrete, and historically grounded, it captures both the
beauty and the practicality of perturbation theory. Personally, I can't wait to
teach from it — and to keep learning from it."
-Steven Strogatz, Cornell University
Perturbation methods are old but powerful, and they remain in widespread use.
Rather than producing numbers or pictures, they yield formulas whose value
depends on the skill of the person (or machine!) interpreting them.
This unique book presents several classical methods for solving perturbation
problems. To ensure a uniform presentation and more reliable, interpretable
results, it consistently uses backward error analysis. This provides a systematic
way to assess the validity of approximate solutions while encouraging the
modeler to examine how small changes in the data or model affect the result.
To support this, the book uses the concept of a condition number, familiar from
numerical analysis.
Perturbation Methods Using Backward Error includes a chapter on the relatively
novel renormalization group method, uses computer algebra (via Maple) to
ease the computing of symbolic answers, provides solutions to all exercises,
and discusses the impact on science of the idea of perturbation.
2026 / 443 pages / Softcover / 978-1-61197-885-8 / List $81.00 / SIAM
Member $56.70 / MM25
Bookstore link:
https://epubs.siam.org/doi/book/10.1137/1.9781611978865
-------------------------------------------------------
From: Qi Tang qtang@gatech.edu
Date: May 13, 2026
Subject: MFEM Community Workshop Sep 22-25, 2026
The MFEM team and Georgia Tech’s School of Computational
Science and Engineering invite you to the 2026 MFEM
Community Workshop, Sep 22–25 at Georgia Tech, with a
virtual attendance option. Sep 22 will feature a hands-on
MFEM tutorial.
Register by Sep 11: https://forms.gle/sGtQrPFAMrgN1Kp59
In-person attendance fees: $150 (regular), $75 (students)
For full details, please visit https://mfem.org/workshop
Program highlights:
* Free tutorial with separate registration (Sep 22):
https://rb.gy/1w59h4
- Hybrid: Georgia Tech or Webex
- Ideal for new users
* Project news and roadmap
* Developer talks (submit abstract with registration)
* Student lightning talks (submit your abstract)
* In-person poster session (submit your abstract)
* Visualization contest (submit your images/animations)
* Office hours
We look forward to engaging with you at the workshop!
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From: Daniel Wachsmuth daniel.wachsmuth@uni-wuerzburg.de
Date: May 11, 2026
Subject: Workshop on Optimization with Differential Equations, Wuerzburg, Sept 16-18 2026
Annual workshop of the GAMM Activity Group on Optimization with
Partial Differential Equations.
This workshop brings together researchers working on optimization
problems constrained by partial differential equations. It provides
a forum for exchanging new results and fostering collaboration
across analysis, numerical methods, and applications.
Contributions from all areas of PDE‑constrained optimization are
welcome.
Invited speakers:
Alberto de Marchi (University of the Bundeswehr Munich, Germany)
Carmen Gräßle (TU Braunschweig, Germany)
Philipp Guth (Austrian Academy of Sciences, Austria)
Johannes Haubner (University of Graz, Austria)
More information can be found on the website:
https://www.mathematik.uni-wuerzburg.de/en/schools/optimization-
with-partial-differential-equations/
Registration is open until July 24th.
Looking forward to see you in Wuerzburg!
Daniel Wachsmuth
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From: Karim Cherifi karim.cherifi@supmicrotech.fr
Date: May 11, 2026
Subject: PhD position in Scientific Machine Learning (SciML) in FEMTO-ST, France
I’m happy to share that we are recruiting a fully funded PhD student at FEMTO-
ST Institute and SUPMICROTECH, co-supervised by Jean-Julien Aucouturier and
myself.
𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Besançon, France
𝗦𝘁𝗮𝗿𝘁 𝗱𝗮𝘁𝗲: September 2026 (flexible)
𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻: 3 years
𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗲𝗮𝗱𝗹𝗶𝗻𝗲: June 15, 2026
𝗣𝗵𝗗 𝗧𝗼𝗽𝗶𝗰: 𝗛𝗮𝗿𝗱 𝗮𝗻𝗱 𝗦𝗼𝗳𝘁 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 𝗶𝗻 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝗳𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
The project focuses on one of the key challenges in Physics-Informed and
Scientific Machine Learning: “How should physical constraints be integrated
into machine learning models?”
The research will involve:
- Physics-informed ML and system identification
- Dynamical systems and port-Hamiltonian formulations
- Benchmarking constrained learning methods with applications in soft
robotics and neuroscience
The position is ideal for candidates with a strong background in:
• Machine Learning
• Control Theory
• Applied Mathematics and Scientific Computing
Strong Python/ML programming skills and prior research experience are highly
appreciated.
To apply, candidates should send: a CV, Cover letter and References to:
karim.cherifi (at) supmicrotech.fr with the Subject: [PhD position] Your Name
Please feel free to share this opportunity with interested students and
researchers in SciML, control, and physics-informed AI communities.
-------------------------------------------------------
From: Babak Maboudi Afkham babak.maboudi@oulu.fi
Date: May 11, 2026
Subject: PhD Position (3 years) in Applied Mathematics – University of Oulu, Finland
The University of Oulu is inviting applications for a fully funded 3-year PhD
position in Applied Mathematics within the project:
“Sparse Measurement Strategies for Goal-Oriented Inverse Problems”
The project is part of the SPARSe Academy Fellowship project and
connected to the Finnish FAME Flagship (Flagship of Advanced Mathematics
for Sensing, Imaging and Modelling).
Research topics include:
-inverse problems
-uncertainty quantification
-Bayesian methods
-numerical analysis and scientific computing
-PDE-based computational modelling
-sparse and optimal measurement strategies
Applications involve medical imaging, X-ray computed tomography, and
seismic imaging.
The position offers:
-an active international research environment in inverse problems and
computational mathematics
-opportunities for international collaboration and yearly academic visits
-participation in leading conferences in applied mathematics and inverse
problems
-access to national high-performance computing infrastructure
-opportunities to contribute to open-source scientific software
We welcome applicants with a strong background in applied mathematics,
numerical analysis, scientific computing, PDEs, inverse problems, probability,
optimization, or related areas.
Application deadline: June 1, 2026
Further details and application:
https://www.linkedin.com/feed/update/urn:li:activity:7459508958647508992
Informal inquiries are welcome:
Babak Maboudi Afkham
babak.maboudi@oulu.fi
-------------------------------------------------------
From: Per Christian Hansen pcha@dtu.dk
Date: May 11, 2026
Subject: PhD Position: Error Propagation and Implicit Priors, DTU, Denmark
The Technical University of Denmark has an opening for a 3-year PhD
position. It is part of the project DUDE, Data Uncertainty and Design/
Reconstruction Errors, headed by Prof. Per Christian Hansen.
This position focuses on analysis of the influence of data errors in
iterative solvers for inverse problems such as computed tomography and
image deblurring. The goal is to establish a solid understanding of
how data errors propagate in iterative regularization methods,
especially CGLS and GMRES. In addition, we aim to interpret these
methods in the framework of computational uncertainty quantification.
For more details and to apply:
https://efzu.fa.em2.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_20
01/job/7344/
The applicants will work together with two postdocs in the Section for
Scientific Computing. Applicants will make limited contributions to
teaching/training activities and supervision of students.
The deadline of applications is June 10, 2026, at 23:59 (Danish time).
-------------------------------------------------------
From: Luca Heltai luca.heltai@unipi.it
Date: May 14, 2026
Subject: 5 PhD Positions in High Performance Scientific Computing – University of Pisa
Applications are now open for the 42nd cycle of the PhD programme in High
Performance Scientific Computing (HPSC) at the University of Pisa.
The programme focuses on advanced methods and technologies for scientific
computing, including:
- numerical methods for PDEs and multiphysics problems,
- high performance computing and parallel algorithms,
- scientific machine learning and AI for computational science,
- large-scale simulation and optimization,
- uncertainty quantification and data-driven modeling.
The PhD is strongly interdisciplinary and involves collaborations with academic
departments, research centers, and industrial partners. Several projects are
directly connected to real-world HPC applications in engineering, climate
science, biomedical modeling, computational physics, and related areas.
The programme is aimed at students interested in research at the intersection
of numerical analysis, scientific computing, and advanced computational
technologies.
Additional information, application deadlines, and the official call are available
here:
https://www.dm.unipi.it/phd-hpsc/call-for-applications-to-the-ph-d-
programme-in-hpsc-42nd-cycle/
Please feel free to circulate this announcement to potentially interested
students and collaborators.
-------------------------------------------------------
From: Debasish Das debasish.das@strath.ac.uk
Date: May 11, 2026
Subject: PhD and Postdoc position in Mathematics at Strathclyde University
Applications are invited for two positions in the Department of
Mathematics and Statistics at the University of Strathclyde,
Glasgow:
- A fully funded three-year PhD position open to international
students
- A three-year PDRA position
These positions are supported by the Leverhulme Trust and focus on
the mathematical and computational modelling of electrically driven
helical microswimmers and related active matter systems.
The PhD project will involve theoretical and computational work,
including asymptotic methods, hydrodynamics, and the modelling of
propulsion and interactions in viscous flows. International
students are welcome to apply.
The PDRA position will concentrate on high-fidelity numerical
simulations of self-propelled helices and related active matter
systems in three-dimensional fluids.
Please feel free to share this post with anyone who may be
interested.
Further details are available here: https://lnkd.in/eV-Fkd29
PhD application: https://www.strath.ac.uk/studywithus/
postgraduateresearchphdopportunities/science/mathematicsstatistics/
activeself-propelledhelices/
PDRA application: https://strathvacancies.engageats.co.uk/
Vacancies/I/6585/0/468090/15019/postdoctoral-research-associate-in-
applied-and-computational-mathematics-814375
-------------------------------------------------------
From: Behnam Hashemi b.hashemi@leicester.ac.uk
Date: May 13, 2026
Subject: Postdoc position in Numerical Linear Algebra at University of Leicester
Application deadline: Tuesday, 2 June 2026
Interviews: Late June or early July 2026
Start date: 1 August 2026
Contract: Fixed-term until 31 May 2029
A Postdoctoral Research Associate position is available to work with Dr
Behnam Hashemi on the EPSRC-funded New Investigator Award Project,
Rigorous Numerics for Transcendental Functions of Matrices.
The project aims to develop verified algorithms for computing matrix
functions with applications to monitoring the quality of results produced by
standard floating-point algorithms and to computer-assisted mathematical
proofs in dynamical systems and ordinary and partial differential equations.
The role will involve developing rigorous a posteriori forward error bounds for
matrix functions using interval analysis, as well as contributing to open-
source software development in MATLAB and Python.
The successful candidate will join the Scientific Computing and Applications
Research Group in the School of Computing and Mathematical Sciences and
will have opportunities to collaborate with project partners in Germany and
the UK.
Applicants should have a PhD in Mathematics or a closely related field,
together with a strong track record in research and publication. Relevant
expertise includes numerical linear algebra, floating-point arithmetic, interval
arithmetic and dynamical systems. Excellent communication and
presentation skills are required.
For informal enquiries, please contact Dr. Behnam Hashemi at
b.hashemi@le.ac.uk.
Application URL: https://jobs.le.ac.uk/vacancies/13255/research-associate-
in-rigorous-numerics-for-matrix-functions.html
-------------------------------------------------------
From: Kirk M Soodhalter soodhalk@tcd.ie
Date: May 08, 2026
Subject: Assistant Professor in High-performance Computing at Trinity College Dublin
Dear colleagues,
The School of Mathematics at Trinity College Dublin is inviting applications for
the post of Assistant Professor in High-performance Computing. The
appointment will be tenable from January 2027. This is a teaching and research
position; the successful candidate will be required to contribute at all levels of
teaching and supervision undertaken by the School and will be expected to
conduct a vigorous research programme. Further details about the position can
be found at
https://my.corehr.com/pls/trrecruit/erq_jobspec_details_form.jobspec?
p_id=038968
while the closing date for applications is July 22nd, 2026. I paste a blurb from
the job description below the closing salutation.
Informal enquiries can directed to headmath@maths.tcd.ie .
Please bring this position to the attention of any potentially interested
candidates.
Sincerely,
Kirk M Soodhalter
Job description:
The School of Mathematics is seeking applications for an Assistant Professor in
High-performance Computing. The successful candidate will have
demonstrated exceptional research promise in Computational
Mathematics/Numerical Analysis with experience in High-performance
Computing applications. With the opening of this position, we seek to expand
our existing research strengths in this direction. The School of Mathematics,
which at Trinity includes Theoretical Physics, boasts research groups in
Applied and Numerical Linear Algebra (ANLA) as well as a computationally-
focused high-energy physics group working in Lattice QCD. The School seeks
to augment its existing strengths in both Mathematics and Theoretical Physics
by further building computational and high-performance computing expertise
while complementing the work of our Lattice and ANLA groups. The post
combines research and teaching; the appointee will contribute to
undergraduate and postgraduate education and supervision, including to
students on the School's taught MSc degree in High Performance Computing,
with particular interests in candidates who are able to offer/develop lectures
complementary to those in our existing M.Sc. in HPC. Candidates working in
any area within Computational Mathematics/Numerical Analysis will be
considered, with preference for those with experience in High-Performance
Computing applications. In particular, candidates working in areas concerning
large-scale computations in optimisation and stochastic partial-differential
equations are encouraged to apply.
-------------------------------------------------------
From: Charley Denton cdenton@aimsciences.org
Date: May 14, 2026
Subject: Contents, AIMS New Article: ACSE Vol. 8, Art. 3
Advances in Computational Science and Engineering
Volume: 8, Art. 3
June 2026
https://www.aimsciences.org/ACSE/article/2026/8/0
Physics informed neural network framework for modified kawahara equations
Santosh Anand, Srinivasan Natesan and Şuayip Toprakseven
-------------------------------------------------------
From: Charley Denton cdenton@aimsciences.org
Date: May 14, 2026
Subject: Contents, AIMS New Articles: CAC Vol. 9, Art. 1, 3
Communications on Analysis and Computation
Volume: 9, Art. 1, 3
September 2026
https://www.aimsciences.org/CAC/article/2026/9/0
Sigmoid mass function generation for multi-ordinal classification model fusion
Shuhui Bi, Yang Cao, Tao Shen, Kang Zhao and Liyao Ma
Fast implementation of nonlinear fractional diffusion equations with time delay
on unbounded spatial domain
Jing Li, Fengping Mao and Zhenrong Chen
-------------------------------------------------------
From: Charley Denton cdenton@aimsciences.org
Date: May 14, 2026
Subject: Contents, AIMS New Article: FAM Vol. 1, Art. 4
Frontiers in Applied Mathematics
Volume: 1, Art. 4
June 2026
https://www.aimsciences.org/FAM/article/2026/1/0
Quantitative photoacoustic imaging in three-dimensional elastic media
Tian Ding and Yan Ma
-------------------------------------------------------
From: Charley Denton cdenton@aimsciences.org
Date: May 14, 2026
Subject: Contents, AIMS New Article: MFC Vol. 13, Art. 1
Mathematical Foundations of Computing
Volume: 13, Art. 1
October 2026
https://www.aimsciences.org/mfc/article/2026/13/0
Approximation via Erdélyi–Kober type Szász–Kantorovich operator
Pinakadhar Baliarsingh and Subhasmita Maharana
-------------------------------------------------------
From: Tristan van Leeuwen t.van.leeuwen@cwi.nl
Date: May 11, 2026
Subject: Call for papers, Springer Künstliche Intelligenz special issue AI4Science
We are happy to announce a special issue on Scientific Machine Learning for
the Künstliche Intelligenz journal (Springer). Its aim is to showcase recent
methodological developments, theoretical insights, and application driven
advances at the interface of numerical analysis, scientific computing, and
machine learning.
Tentative timeline
- Submission deadline: July 15, 2026
- First review round: October 15, 2026
- Final acceptance: December 15, 2026
More details can be found here: https://link.springer.com/collections/ibcajgbjgj
With best regards,
Victorita Dolean (Eindhoven U. of Technology, the Netherlands)
Alexander Heinlein (Delft U. of Technology, the Netherlands)
Alena Kopanicakova (University of Toulouse, France)
Tristan van Leeuwen (Centrum Wiskunde & Informatica and Utrecht U., the
Netherlands)
-------------------------------------------------------
From: Marc Van Barel marc.vanbarel@cs.kuleuven.be
Date: May 13, 2026
Subject: Call for papers: Special Issue of the Journal Numerical Algorithms on the occasion of the 65th anniversary of Nicola Mastronardi
Nicola Mastronardi is a prolific researcher in numerical linear algebra, with a
focus on matrix computations, algorithms for structured rank matrices (like
semiseparable and Toeplitz matrices), eigenvalue problems, and scientific
computing applications.
He has co-authored numerous highly-cited scientific papers and collaborated
with researchers from various international institutions. His work involves
developing efficient algorithms for complex mathematical problems in fields
such as medical diagnostics (e.g., magnetic resonance spectroscopy).
Topics of this special issue are, but not limited to, Numerical Linear Algebra,
Numerical Analysis, Systems and Control Theory, Scientific Computing,
Orthogonal Polynomials.
Submissions should be following the guidelines of the Journal Numerical
Algorithms and can be send in till the end of January 2027 using the
submission system of the journal. The corresponding collection is called
“NM65”.
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End of Digest
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