June 2021 - Machine-learning-nederland

Machine Learning specialist (permanent) - scientific software, SCM, Amsterdam
by Stan Van Gisbergen 21 Sep '21

21 Sep '21

We’re looking for a scientific software developer with Machine Learning (ML) expertise (MSc or PhD in ML). Together with our current mathematics and Machine Learning developers, you will work on developing new functionality where state-of-the-art ML techniques are applied to problems in computational chemistry. https://www.scm.com/news/job-opening-software-developer-machine-learning-in… Dr. S.J.A. van Gisbergen Directeur Software for Chemistry & Materials B.V. De Boelelaan 1083 1081 HV Amsterdam, The Netherlands E-mail: vangisbergen(a)scm.com http://www.scm.com

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Thematic seminar: Gergely Neu, Fri June 25, 16h00
by Tim van Erven 24 Jun '21

24 Jun '21

Dear all, Before the summer really starts, we have a very interesting invited speaker in the thematic seminar on Friday next week: *Gergely Neu* (Universitat Pompeu Fabra, http://cs.bme.hu/~gergo/) * Friday June 25*, 16.00-17.00 Online on Zoom: https://uva-live.zoom.us/j/81805477265 Meeting ID: 818 0547 7265 Please also join for online drinks after the talk. *Information-Theoretic Generalization Bounds for Stochastic Gradient Descent* We study the generalization properties of the popular stochastic gradient descent method for optimizing general non-convex loss functions. Our main contribution is providing upper bounds on the generalization error that depend on local statistics of the stochastic gradients evaluated along the path of iterates calculated by SGD. The key factors our bounds depend on are the variance of the gradients (with respect to the data distribution) and the local smoothness of the objective function along the SGD path, and the sensitivity of the loss function to perturbations to the final output. Our key technical tool is combining the information-theoretic generalization bounds previously used for analyzing randomized variants of SGD with a perturbation analysis of the iterates. Seminar organizers: Tim van Erven Botond Szabo -- Tim van Erven <tim(a)timvanerven.nl> www.timvanerven.nl

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Postdoc position (f/m) in healthcare optimisation at the University of Twente
by r.j.boucherie＠utwente.nl 22 Jun '21

22 Jun '21

The Department of Applied Mathematics of the University of Twente has a vacancy for a Postdoc position (f/m) in mathematics of multi-appointment planning with applications in healthcare. The position is embedded in both Operations Research and the Center for Healthcare Operations Improvement and Research (CHOIR). For details, see https://www.utwente.nl/en/organisation/careers/!/2021-405/postdoc-mathemati… -------------------------------------- Prof.dr. R.J. Boucherie people.utwente.nl/r.j.boucherie<http://people.utwente.nl/r.j.boucherie> Stochastic Operations Research Department of Applied Mathematics University of Twente www.utwente.nl/ewi/sor<http://www.utwente.nl/ewi/sor> CHOIR (Center for Healthcare Operations Improvement & Research) www.utwente.nl/choir<http://www.utwente.nl/choir> P.O. Box 217 NL-7500 AE Enschede P: +31 53 489 3432 E-mail: r.j.boucherie(a)utwente.nl<mailto:r.j.boucherie@utwente.nl> https://people.utwente.nl/r.j.boucherie

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CWI-SC seminar Christian Franzke
by Wouter Edeling 21 Jun '21

21 Jun '21

Dear All, The next speaker in our CWI seminar for Machine Learning and Uncertainty Quantification for Scientific Computing will be Christian Franzke from IBS Center for Climate Physics at the Pusan National University in South Korea. The talk will be on Wednesday the 23rd, at 10AM CEST. The topic will be the detection of causal relationships and physically meaningful patterns from the complex climate system, using reservoir computing and multi-resolution dynamic mode decomposition. The zoom link and abstract can be found below. As usual, feel free to share the zoom link with your colleagues. Kind regards, Wouter Edeling 23 Jun. 2021 10h00 CET: Christian Franzke (IBS Center for Climate Physics, Pusan National University in South Korea): Causality Detection and Multi-Scale Decomposition of the Climate System using Machine Learning Detecting causal relationships and physically meaningful patterns from the complex climate system is an important but challenging problem. In my presentation I will show recent progress for both problems using Machine Learning approaches. First, I will show that Reservoir Computing is able to systematically identify causal relationships between variables. I will show evidence that Reservoir Computing is able to systematically identify the causal direction, coupling delay, and causal chain relations from time series. Reservoir Computing Causality has three advantages: (i) robustness to noisy time series; (ii) computational efficiency; and (iii) seamless causal inference from high-dimensional data. Second, I will demonstrate that Multi-Resolution Dynamic Mode Decomposition can systematically identify physically meaningful patterns in high-dimensional climate data. In particular, Multi-resolution Dynamic Mode Decomposition is able to extract the changing annual cycle. Join Zoom Meeting https://cwi-nl.zoom.us/j/81854110402?pwd=YTBvNU9qWHlBaVA2aURISGtKeitSUT09 Meeting ID: 818 5411 0402 Passcode: 599921

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Vacancy PhD student Ghent University
by Stijn Luca 09 Jun '21

09 Jun '21

A vacancy for an assistant is available at our department (Data Analysis and Mathematical Modelling) of Ghent University with a 50% research / 50% teaching assignment. The PhD topic will be situated within the field of extreme value statistics while exploring links with computational and machine learning methods. Deadline for application is 22-06-2021. For more info and the application procedure, click here<https://career012.successfactors.eu/career?company=C0000956575P&career_job_…> For more info about the PhD topic and our group www.biostat.ugent.be<http://www.biostat.ugent.be> [cid:image001.png@01D75C87.92473700]<https://www.ugent.be/bw> Prof. dr. Stijn Luca Assistant professor T +32 9 264 59 34 Department Data Analysis and Mathematical Modelling Research Unit BIOSTAT: Biostatistics www.biostat.ugent.be<http://www.biostat.ugent.be/> Campus Coupure, Block A 1st floor 110.068, Coupure links 653, B-9000 Ghent, Belgium directions<https://www.google.com/maps/dir/Coupure+Links+653,+9000+Gent/@51.0527795,3.…> T administration office +32 9 264 59 32 www.ugent.be<https://www.ugent.be/bw/en> e-maildisclaimer<https://helpdesk.ugent.be/e-maildisclaimer.php>

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CWI-SC seminar Hannah Christensen (Oxford)
by Wouter Edeling 04 Jun '21

04 Jun '21

Dear all, On Thursday June 10th, at 4PM CET, Hannah Christensen from the University of Oxford will give the next talk in the CWI seminar on Machine Learning and Uncertainty quantification for Scientific computing. She will talk about stochastic subgrid-scale parametrisation using GANs, applied to atmospheric models. The abstract and zoom link can be found below. Feel free to share the zoom link with anyone who you think would be interested. Kind regards, Wouter Edeling 10 Jun. 2021 16h00 CET: Hannah Christensen (Oxford): Machine Learning for Stochastic Parametrisation Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale motion is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale-separation in the atmosphere means that this approach is a large source of error in forecasts. Over the last decade an alternative paradigm has developed: the use of stochastic techniques to characterise uncertainty in small-scale processes. These techniques are now widely used across weather, seasonal forecasting, and climate timescales. While there has been significant progress in emulating parametrisation schemes using machine learning, the focus has been entirely on deterministic parametrisations. In this presentation I will discuss data driven approaches for stochastic parametrisation. I will describe experiments which develop a stochastic parametrisation using the generative adversarial network (GAN) machine learning framework for a simple atmospheric model. I will conclude by discussing the potential for this approach in complex weather and climate prediction models. Join Zoom Meeting https://cwi-nl.zoom.us/j/86156254037?pwd=QnpVTlZqdnpnaDdvNm83TlM3MTFZUT09 Meeting ID: 861 5625 4037 Passcode: 364032

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Machine-learning-nederland June 2021