[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) Subscribe, unsubscribe, change address, or for na-digest archives: http://www.netlib.org/na-digest-html/faq.html Submissions for NA Digest: http://icl.utk.edu/na-digest/ ------------------------------------------------------- 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/ ------------------------------------------------------- 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] ------------------------------------------------------- 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]). ------------------------------------------------------- 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. ------------------------------------------------------- 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 ------------------------------------------------------- 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] ------------------------------------------------------- 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/ ------------------------------------------------------- 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 ------------------------------------------------------- End of Digest **************************