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NA Digest, V. 20, # 23

NA Digest Sunday, June 14, 2020 Volume 20 : Issue 23


Today's Editor:

Daniel M. Dunlavy
Sandia National Labs
dmdunla@sandia.gov

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From: Matthias Maier maier@math.tamu.edu
Date: June 10, 2020
Subject: Finite Element Software deal.II Version 9.2.0 released


Version 9.2.0 of deal.II, the object-oriented finite element library
awarded the J. H. Wilkinson Prize for Numerical Software, has been
released. It is available for free under an Open Source license from
the deal.II homepage at https://www.dealii.org/

The major changes of this release are:
- Seven new tutorial programs: step-47 solves the biharmonic equation;
step-50 demonstrates algebraic and geometric multigrid methods for
large, parallel computations on adaptively refined meshes, and
compares matrix-based and matrix-free implementations; step-58
solves the nonlinear Schroedinger equation; step-65 illustrates
working with complex geometries and curved domains; step-67 and
step-69 implementing different approaches for the Euler equations in
compressible gas dynamics; step-70 illustrates flow around a moving
obstacle.
- Substantial improvements to the Python interfaces, including Jupyter
versions of the step-49 and step-53 tutorial program.
- A new triangulation class (parallel::fullydistributed::Triangulation)
that completely distributes a triangulation, rather than keeping the
coarse mesh available on all processors.
- The DataOut and related classes now fully support outputting
complex-valued solution vectors, including complex-valued vector and
tensor fields.
- A number of fixes throughout the library for problems with more than
2^32 (=4 billion) unknowns.
- Improvements to the support for particle based methods as well as to
parallel hp-adaptive finite element methods.
- More than 320 other new features, improvements, and bugfixes.

For more information see
- the preprint at https://www.dealii.org/deal92-preprint.pdf
- the list of changes at
https://www.dealii.org/developer/doxygen/deal.II/changes_between_9_1_1_and_9_2_0.html

The main features of deal.II are: Extensive documentation and 66
fully-functional example programs; Support for dimension-independent
programming; Locally refined adaptive meshes; Multigrid support; A zoo
of different finite elements; Fast linear algebra; Built-in support
for shared memory and distributed parallel computing, scaling from
laptops to clusters with 100,000+ processor cores; Interfaces to
Trilinos, PETSc, METIS, UMFPACK and other external software; Output
for a wide variety of visualization platforms.



From: Russell Luke r.luke@math.uni-goettingen.de
Date: June 11, 2020
Subject: New Book, Nanoscale Photonic Imaging


Nanoscale Photonic Imaging,
Tim Salditt, Alexander Egner and D. Russell Luke (eds)
Topics in Applied Physics, vol 134. Springer, Cham
https://doi.org/10.1007/978-3-030-34413-9
24 chapters, 634 pages.

This open access book, edited and authored by a team of world-leading
researchers, provides a broad overview of advanced photonic methods
for nanoscale visualization, as well as describing a range of
fascinating in-depth studies. Introductory chapters cover the most
relevant physics and basic methods that young researchers need to
master in order to work effectively in the field of nanoscale photonic
imaging, from physical first principles, to instrumentation, to
mathematical foundations of imaging and data analysis. Subsequent
chapters demonstrate how these cutting edge methods are applied to a
variety of systems, including complex fluids and biomolecular systems,
for visualizing their structure and dynamics, in space and on
timescales extending over many orders of magnitude down to the
femtosecond range.

Progress in nanoscale photonic imaging in Göttingen has been the sum
total of more than a decade of work by a wide range of scientists and
mathematicians across disciplines, working together in a vibrant
collaboration of a kind rarely matched. It serves not only as a useful
reference for experienced researchers but also as a valuable point of
entry for newcomers.



From: Lieven De Lathauwer lieven.delathauwer@kuleuven.be
Date: June 11, 2020
Subject: Tenure-Track Position, Computational ML, KU Leuven, Belgium


The Faculty of Engineering Science at KU Leuven is seeking to fill the
position of a tenure-track assistant professor in "Computational
Machine Learning". The successful candidate will join the STADIUS
Center for Dynamical Systems, Signal Processing, and Data Analytics in
the Department of Electrical Engineering (ESAT).

ESAT-STADIUS pursues excellence in an explicit and synergistic
combination of fundamental and applied research. With core concepts
from linear and multi-linear algebra, statistics, optimization,
machine learning, and artificial intelligence, its fundamental
research is focused on the development of mathematical engineering
tools and numerical algorithms. Building upon this foundation, applied
research aims to advance the current state of technology across a wide
range of relevant application fields, including industrial automation
and control, speech and audio signal processing, digital
communications, biomedical data analysis and signal processing,
bioinformatics and systems biology.

The candidate will establish an impactful mathematical engineering
research programme focusing on computational and theoretical aspects
of machine learning, further strengthening and complementing the
current fundamental research activities within ESAT-STADIUS. His/her
research programme will also be relevant to the range of application
fields currently covered by ESAT-STADIUS, with preference given to
applications in big data and e-health, e.g., clinical diagnostics,
decision support and personalized medicine. The candidate will provide
high-quality teaching in the Bachelor and/or Master programmes of the
Faculty of Engineering Science, including mathematical engineering
courses.

KU Leuven seeks to foster an environment where all talents can
flourish, regardless of gender, age, cultural background, nationality
or impairments.

For detailed information, please visit:
https://www.kuleuven.be/personeel/jobsite/jobs/55579156?hl=en&lang=en

Application deadline: August 15, 2020.



From: Josh Stevens cmih@maths.cam.ac.uk
Date: June 12, 2020
Subject: Postdoc Position, Data Science for Healthcare, Cambridge Univ


We invite applications for a Post Doctoral Research Associate to work
in the EPSRC Cambridge Mathematics of Information in Healthcare Hub
(CMIH) at the University of Cambridge.

The Hub is a collaboration between mathematics, statistics, computer
science, medicine, and clinicians, and aims to develop rigorous and
clinically practical algorithms for analysing healthcare data for
personalised diagnosis and treatment as well as target identification
and validation at the population level. Furthermore, this will focus
on some of the most challenging public health problems, namely:
cancer, cardiovascular disease, and dementia.

Applicants must have (or be about to receive) a PhD degree in
mathematics or statistics (or a closely related discipline). The ideal
candidate will be experienced in one or more of the following areas:
statistical shape analysis, functional data analysis, longitudinal
data analysis, machine learning, inverse problems, computational
analysis, optimisation and/or data science. Experience in parallel
computing and C programming skills are desirable.

The closing date for applications is 12th July 2020.

Informal enquiries can be made to: LE23160@maths.cam.ac.uk

For further information and application instructions please visit
http://www.jobs.cam.ac.uk/job/25962/



From: Stephen Langdon stephen.langdon@brunel.ac.uk
Date: June 08, 2020
Subject: PhD Position, Brunel Univ London


Applications are invited for the PhD project:
“Deep Learning for Inverse Scattering Problems”

For full details, please see:
https://www.brunel.ac.uk/research/Research-degrees/PhD-Studentships/Studentship?
id=8728e26a-4f02-400b-af85-263f0556bda9

Closing date 26th June 2020

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