[External Email]

Subject: NA Digest, V. 21, # 2

NA Digest  Monday, January 11, 2021  Volume 21 : Issue 2

Today's Editor:

        Daniel M. Dunlavy
        Sandia National Labs
        [log in to unmask]

Today's Topics:

        Safety and Security of Deep Learning, ONLINE, Apr 2021
        Research Scientist Position, System Modeling, Motional
        Research Scientist Positions, Comp Vision/ML/Wavelet/Optim, SZU
        Postdoc Positions, NRC, NIST
        Postdoc Positions, Univ of Coimbra, Portugal
        PhD/Postdoc Position, Mathematical Optimization, Germany
        Intern Positions, FiveAI
        Contents, Numerical Algorithms, 86 (1)

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From: Ruth Crane [log in to unmask]
Date: January 11, 2021
Subject: Safety and Security of Deep Learning, ONLINE, Apr 2021

Deep learning is profoundly reshaping the research directions of
entire scientific communities across mathematics, computer science,
and statistics, as well as the physical, biological and medical
sciences . Yet, despite their indisputable success, deep neural
networks are known to be universally unstable. That is, small changes
in the input that are almost undetectable produce significant changes
in the output. This happens in applications such as image recognition
and classification, speech and audio recognition, automatic diagnosis
in medicine, image reconstruction and medical imaging as well as
inverse problems in general. This phenomenon is now very well
documented and yields non-human-like behaviour of neural networks in
the cases where they replace humans, and unexpected and unreliable
behaviour where they replace standard algorithms in the sciences.

The many examples produced over the last years demonstrate the
intricacy of this complex problem and the questions of safety and
security of deep learning become crucial. Moreover, the ubiquitous
phenomenon of instability combined with the lack of interpretability
of deep neural networks makes the reproducibility of scientific
results based on deep learning at stake.

For these reasons, the development of mathematical foundations aimed
at improving the safety and security of deep learning is of key
importance.  The goal of this workshop is to bring together experts
from mathematics, computer science, and statistics in order to
accelerate the exploration of breakthroughs and of emerging
mathematical ideas in this area.

This ICERM workshop is fully funded by a Simons Foundation Targeted
Grant to Institutes.
Apply today! https://icerm.brown.edu/events/htw-21-ssdl/


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From: Anne Collin [log in to unmask]
Date: January 08, 2021
Subject: Research Scientist Position, System Modeling, Motional

More information about Motional: https://motional.com/

Autonomous vehicles are complex systems operating in complex
environments. The Rulebooks team defines and assesses behavior of our
autonomous vehicles.This role involves gathering relevant information
from different technical teams, understanding the autonomous vehicle
systems, and making autonomous vehicle system models to support high-
level design decisions.

In this role you will: Identify key design decisions that greatly
impact autonomous driving performance, as defined by the Rulebooks
team; Model the hardware and software components at a deep-enough
level that the aggregate behavior of the AV is captured; Present model
assumptions and resulting tradeoffs to various teams; Visualize impact
of different architecture or design choices.

We are looking for: PhD in Robotics, Applied Mathematics, Aerospace,
Systems Engineering, Operations Research, or any relevant field;
Python knowledge; Experience with discrete modeling techniques, such
as stochastic processes; Experience working in interdisciplinary
research, and ability to communicate about your work with both subject
matter experts and non technical teams; Background in robotics systems
is helpful, but not required; Experience with safety-critical systems
or standards-regulated industries is a plus.

If interested, please reach out directly to [log in to unmask]


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From: J. Lu [log in to unmask]
Date: January 07, 2021
Subject: Research Scientist Positions, Comp Vision/ML/Wavelet/Optim, SZU

Job Type: Full-Time for Research Scientists
Duration: 3--5 years
Number of Position: 2 Positions

Salary for Reseach Scientist: about 310,000 RMB (48,000 US dollars) --
700,000 RMB (108,000 US dollars) per year based on the academic
evaluation of candidates.

Closing Date: Open Until Filled.

Description: We have projects that are looking for Postdoct/research
scientist in Computer Vision, Image/Video/Signal Processing/Analysis,
Machine Learning (deep learning), Optimization, wavelet analysis, etc.

We have no teaching tasks for Research Scientist Researchers.

Promotors: Prof. Jian Lu (Shenzhen Key Laboratory of Advanced Machine
Learning and Applications, College of Mathematics and Statistics,
Shenzhen University)

Those who are interested in Research Scientist please send their
C.V. to Prof. Dr. Jian Lu (email: [log in to unmask];
[log in to unmask]).


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From: Tim Burns [log in to unmask]
Date: January 05, 2021
Subject: Postdoc Positions, NRC, NIST

The Applied and Computational Mathematics Division (ACMD) of the
National Institute of Standards and Technology (NIST) invites
applications for two-year NRC postdoctoral research positions at NIST
Laboratories in Gaithersburg, Maryland and Boulder, Colorado. NIST is
a Federal government research laboratory specializing in measurement
science.  ACMD consists of some 46 full-time professional staff, along
with part-time faculty appointees and guest researchers. Staff members
engage in collaborative research with scientists throughout NIST,
providing expertise in applied mathematics, mathematical modeling, and
computational science and engineering.

Research areas of interest include complex systems and networks,
computational materials science, computational fluid dynamics,
computational electromagnetics, computational biology, orthogonal
polynomials and special functions, applied optimization and
simulation, combinatorial software testing, data mining and
visualization, parallel and distributed algorithms, quantum
information science, and uncertainty quantification in scientific
computing.

Candidates and their research proposals are evaluated in a competitive
process managed by the National Research Council (NRC) Associateship
Programs. The current stipend is $72,030 per year; there is also a
$5500 travel and equipment allowance. For further details, see
https://www.nist.gov/itl/math/postdoctoral-opportunities. Application
deadlines are February 1 and August 1. Appointments commence within
one year of selection. For questions, contact Tim Burns,
[log in to unmask]

NIST is an equal opportunity employer. The NRC Associateship Program
at NIST is restricted to US citizens.


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From: Ercilia Sousa [log in to unmask]
Date: January 09, 2021
Subject: Postdoc Positions, Univ of Coimbra, Portugal

The Centre for Mathematics of the University of Coimbra invites
applications for two Postdoctoral research grants to be started in
September. The grants are for one year with the possibility of renewal
for one more year.

https://cmuc.mat.uc.pt/rdonweb/even/showPPHighlight.do?highlightID=195

See the official announcement at
www.eracareers.pt/opportunities/index.aspx?task=global&jobId=130091

Candidates with qualifications obtained abroad will have to make proof
that they have their Ph.D. degree recognized by the Portuguese
authorities (Decree-Law no. 66/2018, of 16 August), or provide
evidence that they have requested it, until the end of the application
process. This request can be made online at

https://www.dges.gov.pt/en/pagina/degree-and-diploma-recognition


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From: Russell Luke [log in to unmask]
Date: January 05, 2021
Subject: PhD/Postdoc Position, Mathematical Optimization, Germany

The research group in Mathematical Optimization at the University of
Gottingen is seeking qualified applicants for a PhD or Postdoc
position, depending on experience.  The position is in the context of
the Collaborative Research Center (CRC) 1456, Mathematics of
Experiment, at the University of Gottingen which seeks excellent
candidates to fill 27 PhD positions and 2 Postdoctoral positions as
soon as possible (see https://www.uni-goettingen.de/en/632759.html).

Your profile:
- M.Sc. degree (or equivalent) in mathematics / related field.
- You have a strong interest in mathematical statistics, optimization,
  stochastic processes, scientific computing, machine learning or
  mathematical data analysis, and you like to work with real data.
- You like to work in an interdisciplinary team.
- You are fully proficient in written and spoken English.

The University of Gottingen is an equal opportunity employer and
places particular emphasis on fostering career opportunities for
women. Qualified women are strongly encouraged to apply. We are
committed family-friendly policies and support our employees in
balancing work and family life. We are committed to employing a
greater number of severely disabled persons. Applications from
severely disabled persons with equivalent qualifications will be given
preference.

Interested applicants should contact Prof. Russell Luke:
[log in to unmask]

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From: Jonathan Sadeghi [log in to unmask]
Date: January 07, 2021
Subject: Intern Positions, FiveAI

We'll have a limited number of opportunities for 8-12 weeks for
students at FiveAI.

This could involve you working on machine learning, computer vision,
robotics, 3D graphics, 3D mapping/GIS, control systems or
probabilistic programming.

Requirements: You'll be studying computer science, mathematics,
engineering or physical sciences, with a strong mathematics content,
at a top university.  Strong mathematical background and demonstrable
programming experience in one of C/C++, Python

Closing date: 15 January 2021
For further information please see:
https://apply.workable.com/five-ai-inc/j/7E97A33C17/

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From: Claude Brezinski [log in to unmask]
Date: January 06, 2021
Subject: Contents, Numerical Algorithms, 86 (1)

Table of Contents
Numerical Algorithms, Vol. 86, No. 1

Adaptive total variation and second-order total variation-based model
for low-rank tensor completion, Xin Li, Ting-Zhu Huang, Xi-Le Zhao,
Teng-Yu Ji, Yu-Bang Zheng, Liang-Jian Deng

Convergence analysis of the product integration method for solving the
fourth kind integral equations with weakly singular kernels, Sayed
Arsalan Sajjadi, Saeed Pishbin

Subgradient projection methods extended to monotone bilevel
equilibrium problems in Hilbert spaces, Pham Ngoc Anh, Ho Phi Tu

A numerically efficient variational algorithm to solve a fractional
nonlinear elastic string equation, Jorge E. Macias-Diaz

Flattened aggregate function method for nonlinear programming with
many complicated constraints, Xiaowei Jiang, Yueting Yang, Yunlong Lu,
Mingyuan Cao

Superconvergence analysis of two-grid methods for bacteria equations,
Dongyang Shi, Chaoqun Li

Parallel reduction of four matrices to condensed form for a
generalized matrix eigenvalue algorithm, Nela Bosner

On the parameter selection in the transformed matrix iteration method,
Tahereh Salimi Siahkolaei, Davod Khojasteh Salkuyeh

Projection extragradient algorithms for solving nonmonotone and
non-Lipschitzian equilibrium problems in Hilbert spaces, Lanmei Deng,
Rong Hu, Yaping Fang

A multiresolution algorithm to generate images of generalized fuzzy
fractal attractors, Rudnei D. Cunha, Elismar R. Oliveira, Filip
Strobin

Convergence analysis on matrix splitting iteration algorithm for
semidefinite linear complementarity problems, Yi-Fen Ke

Preconditioners and their analyses for edge element saddle-point
systems arising from time-harmonic Maxwell's equations, Ying Liang,
Hua Xiang, Shiyang Zhang, Jun Zou

A self-adaptive descent LQP alternating direction method for the
structured variational inequalities, Abdellah Bnouhachem

A Riemannian derivative-free Polak-Ribi-re-Polyak method for tangent
vector field, Teng-Teng Yao, Zhi Zhao, Zheng-Jian Bai, Xiao-Qing Jin

Superconvergence in H1-norm of a difference finite element method for
the heat equation in a 3D spatial domain with almost-uniform mesh,
Xinlong Feng, Ruijian He, Zhangxin Chen

Switching preconditioners using a hybrid approach for linear systems
arising from interior point methods for linear programming, Petra
Maria Bartmeyer, Silvana Bocanegra, Aurelio Ribeiro Leite Oliveira

Recovery type a posteriori error estimates for the conduction
convection problem, Qiuyu Zhang, Jian Li, Pengzhan Huang

On block Gaussian sketching for the Kaczmarz method, Elizaveta
Rebrova, Deanna Needell

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