NA Digest Sunday, September 06, 2020 Volume 20 : Issue 34


Today's Editor:

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

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From: Martin Stynes m.stynes@csrc.ac.cn
Date: September 04, 2020
Subject: Numerical Methods for Fractional-Derivative Problems, ONLINE, Oct 2020


The 4th Conference on Numerical Methods for Fractional-Derivative
Problems, a continuation of the annual meetings held at Beijing
Computational Science Research Center in 2017-2019, was postponed from
June 2020. It will now be held online on 22-24 October 2020. This
year's conference, in addition to focusing on fractional derivative
problems, will include a few talks on the closely-related area of
inverse problems.

Main Speakers: Bangti Jin (UK), Natalia Kopteva (Ireland), Abner
Salgado (USA), Masahiro Yamamoto (Japan). There are also several
distinguished Invited Speakers.

Registration for the conference is free of charge, but it is necessary
to register by 8 October to gain access to the conference talks.

If you wish to give a Contributed Talk (25 minutes) at the conference,
submit a title and abstract by 1 October.

All information (registration, abstract submission, etc.) is at the
conference webpage
http://www.csrc.ac.cn/en/event/workshop/2020-01-10/103.html


From: Nicolas Gillis nicolas.gillis@umons.ac.be
Date: September 01, 2020
Subject: Low-Rank Models and Applications, Belgium, Jun 2021


The 2021 Workshop on Low-Rank Models and Applications (LRMA) will take
place from 28-29 June 2021 at the University of Mons, Belgium. The
LRMA workshop is sponsored by the European Research Council (ERC), and
offers a vibrant and intimate venue for interaction between
researchers from fields such as computer science, information theory,
mathematics and signal processing. The scientific program of the LRMA
workshop will include invited plenary lectures, as well as regular
contributed talks and posters. The plenary speakers are: Jerome Bobin
(CEA Saclay), Eric Chi (North Carolina State University), Emilie
Chouzenoux (Inria Saclay), Lieven De Lathauwer (KULeuven), Nicolas
Dobigeon (IRIT/INP-ENSEEIHT, Universite de Toulouse), Kejun Huang
(University of Florida), Yuji Nakatsukasa (University of Oxford), and
Nelly Pustelnik (CNRS, ENS Lyon).

All the information can be found on the following
website:https://sites.google.com/view/lrma21/


From: Heike Sill heike.sill@wias-berlin.de
Date: September 02, 2020
Subject: Research Assistant Position, WIAS, Germany


At WIAS in the research group "Interacting Random Systems" (Head:
Prof. Dr. W. Konig) at the next possible date three positions as
Research Assistant (f/m/d) (Ref. 20/15). To be occupied.

The successful candidates will analyze large interacting random
systems using probabilistic methods within the newly established DFG
priority program SPP 2265 "Random Geometric Systems"
(https://www.wias-berlin.de/projects/SPP2265/). The main research
topics of these positions are phase transitions during coagulation and
condensation. The third position is financed from other funds and
offers more thematic flexibility within the research group and
organizational responsibility. A completed scientific university
degree (master or diploma) is expected as well as a doctorate (or
comparable experience) in mathematics or a closely related
field. experience in the fields of random point processes, large
deviations or statistical physics and phase transitions will
strengthen your application.

See here for more information: https://short.sg/j/7609179


From: Jan S Hesthaven jan.hesthaven@epfl.ch
Date: September 01, 2020
Subject: Postdoc Position, Digital Twin Project, EPFL, CH


In the framework of an exciting industrial grant between Prof. Jan
Hesthaven's group at EPFL and the Morocco based global mining company
OCP, we are looking for a postdoctoral computational scientist/applied
mathematician to work at EPFL on project that seeks to build a digital
twin of a critical mining asset to enable predictive maintenance and
operational optimization.

The project involves a close collaboration with Akselos to provide an
efficient computational backend to drive the digital twin. Akselos is
an MIT spin-off that is providing a revolutionary new reduced order
modeling approach for large-scale engineering simulations. Akselos
headquarters are located on the EPFL campus.

Job description: The successful candidate will join a project focusing
on demonstrating digital twin technology for a real mining asset. This
includes not only the development of an accelerated model, developed
in collaboration with Akselos, but also sensor data integration and
decision support under uncertainty. Focus will be on time-dependent
problems. A PhD in mathematics, mechanical engineering or related
topics; Experience with model order reduction techniques; Experience
with Finite Element Methods (FEM); C++ and/or Python expertise; Some
experience with machine learning for classification is a plus;
Experience with open source C++ FEA or CFD libraries is a plus (e.g.
libMesh, Deal.II, DUNE, OpenFOAM, PETSc, etc); Experience with methods
for time-dependent problems is a plus.

The project is a collaborative project so strong interpersonal and
communication skills are required. Working language is English. There
may be some teaching responsibilities at EPFL as part of the position.

We offer a 1 year contract with the possibility of an extension, a
dynamic and inspiring working environment, and a competitive salary. A
start date of November 1, 2020 is preferred but this is negotiable.

To express your interest, please send a letter of motivation, a
resume, and at least 2 names of references to mcss@epfl.ch. Evaluation
of applications will begin late September and continue until the
position is filled.



From: Jan S Hesthaven jan.hesthaven@epfl.ch
Date: September 01, 2020
Subject: Postdoc Position, ROM for Time-Dependent Problems, EPFL


In the framework of an exciting industrial grant between Prof. Jan
Hesthaven's group at EPFL and the company Akselos, we are looking for
a postdoctoral computational scientist/applied mathematician to work
at EPFL on R&D problems that are relevant to Akselos customers.

Akselos is an MIT spin-off that is providing a revolutionary new
reduced order modeling approach for large-scale engineering
simulations. Akselos has five main offices around the world with
headquarters located on the EPFL campus.

Job description: The component-based reduced basis technology combines
the strengths of domain decomposition and model reduction techniques
in order to reach unprecedented simulation speed (around 1000 times
faster than traditional finite element methods). The successful
candidate will join a 2 year project focused on extending the current
technology to time-dependent problems applied to large-scale wind
turbine applications.

The ideal candidate will have the following skills: A PhD in
mathematics, mechanical engineering or related topics; Experience with
model order reduction techniques; Experience with Finite Element
Methods (FEM); C++ and/or Python expertise; Experience with open
source C++ FEA or CFD libraries is a plus (e.g. libMesh, Deal.II,
DUNE, OpenFOAM, PETSc, etc); Experience with methods for
time-dependent problems is a plus.

The project is a collaborative project so strong interpersonal and
communication skills are required. Working language is English. There
may be some teaching responsibilities at EPFL as part of the position.

We offer a 1 year contract with the possibility of an extension, a
dynamic and inspiring working environment, and a competitive salary. A
start date of November 1, 2020 is preferred but this is negotiable.

To express your interest, please send a letter of motivation, a
resume, and at least 2 names of references to recruitment@akselos.com,
or mcss@epfl.ch. Evaluation of applications will begin late September
and continue until the position is filled.


From: Jan S Hesthaven jan.hesthaven@epfl.ch
Date: September 01, 2020
Subject: Postdoc Position, Scientific Machine Learning, EPFL, CH


We are looking for a postdoctoral computational scientist/applied
mathematician to join the Chair of Computational Mathematics and
Simulation Science, lead by Prof Jan S Hesthaven, to work in the area
of scientific machine learning.

Current activities focus on the development of collaborative modes of
interaction between modern computational methods and recent machine
learning techniques, in particular based on neural networks. Such
developments seek to overcome known algorithmic bottlenecks or enable
entirely new ways of solving problems of relevance to science and
engineering. The postdoctoral fellow is expected to engage in
different projects in line with the above vision.

The ideal candidate will have the following skills: A PhD in
mathematics, mechanical engineering or related topics; Experience with
model order reduction techniques; Experience with modern computational
methods for solving PDEs; Good programming skills.

The effort is of a collaborative nature so strong interpersonal and
communication skills are required. Working language is English. There
will be some teaching responsibilities at EPFL as part of the
position.

We offer a 1 year contract with the possibility of an extension, a
dynamic and inspiring working environment, and a competitive salary. A
start date of January 1, 2021 is preferred but this is negotiable.

To express your interest, please send a letter of motivation, a
resume, and at least 2 names of references to mcss@epfl.ch. Evaluation
of applications will begin late September and continue until the
position is filled.


From: Axel Kroener axel.koener@wias-berlin.de
Date: September 05, 2020
Subject: PhD Position, Optimization, Humboldt Univ Berlin


Job Description: PhD position in the research project 'Equilibria for
Energy Markets with Transport' (Cluster of Excellence MATH+); the
research will concentrate on modeling, analytic, optimization and
numerical aspects of generalized Nash equilibrium problems with
partial differential equation constraints.

Requirements: A completed university degree in mathematics or a
related discipline (preferably with very good results); Knowledge of
partial differential equations, continuous optimization and applied
functional analysis; Experience in the field of numerical and
computer-aided implementation; a high proficiency in spoken and
written English For further information see: http://www.mathplus.de/
oder https://wias- berlin.de/people/hintermueller/

Please send your application until September 23, 2020 and quoting the
reference number DR/146/20 to Humboldt-Universitat zu Berlin, Faculty
of Mathematics and Natural Sciences, Department of Mathematics,
Prof. Dr. Michael Hintermuuller (located: Rudower Chaussee 25,
Berlin-Adlershof), Unter den Linden 6, 10099, or preferably in
electronic form to hint@mathematik.hu-berlin.de (cc to
fiebig@math.hu-berlin.de).



From: Heike Sill heike.sill@wias-berlin.de
Date: September 03, 2020
Subject: PhD Position, WIAS, Germany


WIAS invites applications for a PhD Student Position (f/m/d)
(Ref. 20/17) in the Research Group "Laser Dynamics" (Head: PD Dr. Uwe
Bandelow) starting at the earliest possible date.

Preconditions: A completed scientific university education in the
field of physics or mathematics with good grades. Ability to work on
a theoretical project. Experience with Maxwell equations, modeling of
optical systems, or nonlinear dynamics is an advantage. Experience
with models based on partial differential equations and their
numerical treatment. Provable knowledge of, e.g., Matlab, Python,
C/C++, Mathematica is an advantage. Good English skills and
willingness for a research stay in Australia.

The successful candidate will work on theory of short pulses and
solitons propagating in optical fibers. This project is part of a
collaboration between WIAS and the Institute of Photonics and Optical
Science at the University of Sydney.

The initial appointment is for two years with the possibility of
extension. The reduced work schedule (75 %) is 29.25 hours per week,
and the salary is according to the German TVoeD scale.

Please direct your queries to Priv.-Doz. Dr. Uwe Bandelow
(Uwe.Bandelow@wias-berlin.de).

The Institute aims to increase the proportion of women in this field,
so applications from women are particularly welcome. Among equally
qualified applicants, disabled candidates will be given preference.

Please upload complete application documents including Cover letter,
Curriculum vitae, Photocopies of certificates and transcript of
grades, Diploma/Master thesis, published papers/conference
proceedings, Any other documents that can strengthen your application
and proof the required skills via our website or via our online
job-application facility using the button "Apply online".

The search for a qualified candidate will start immediately and
continue until the position is filled. See here for more information:
https://short.sg/j/7630089

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