- Machine-learning-nederland

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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Assistant/Associate Professor (f/m) in Optimization and Learning at the University of Twente
by r.j.boucherie＠utwente.nl 28 May '21

28 May '21

The Department of Applied Mathematics of the University of Twente has a vacancy for an Assistant or Associate Professor (f/m) in Optimization and Learning with particular focus on mathematical methods for Markov decision theory and reinforcement learning applied in healthcare optimization. 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/!/2021-381/assistantassociat… -------------------------------------- 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 https://people.utwente.nl/r.j.boucherie

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Senior Bioinformatician in Molecular Screening and Omics Research - AstraZeneca - Cambridge, UK
by Sergio Martinez Cuesta 24 May '21

24 May '21

Are you a bioinformatician passionate about medical research and looking for an exciting and impactful new challenge? Do you have experience in analysing *genome-wide omics* data and *high-throughput molecular or genetic screens* for target identification and drug discovery? We are currently seeking a talented *Senior Bioinformatician* to join our Data Sciences & Quantitative Biology team in *Cambridge, UK* and apply bioinformatics and computational solutions to help us evolve and extend our capabilities in our drive to find the medicines of the future. In doing so, you will be part of a multi-disciplinary team of bioinformaticians, data scientists, image analysts and statisticians providing quantitative insights to discover new therapies. Our group is committed to enhancing AstraZeneca’s ability to enable effective target selection and hit identification. We welcome applications no later than *Saturday 5th June*. For more details and to apply, use the following link: https://careers.astrazeneca.com/job/cambridge/senior-bioinformatician-molec…

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PhD position on Data-driven Optimization for Sustainable Last-Mile Delivery at TU/e
by Zhang, Yingqian 18 May '21

18 May '21

(apologies for multiple posting) https://jobs.tue.nl/en/vacancy/phd-on-datadriven-optimization-for-sustainab…<https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fjobs.tue.…> The “Atlas leefbare stad” (Atlas livable city) is a digital twin application that processes logistical data for urban environments. It helps policymakers to gain insight into the current logistical situation, but it can also be used to find the optimal decision with respect to an objective such as fuel consumption or greenhouse gas emission. This makes it possible to try new optimal solutions in a realistic virtual environment, before applying them in practice. As a PhD candidate, you are expected to develop new machine learning driven optimization algorithms, using techniques such as reinforcement learning, online supervised learning, Bayesian optimization, etc., that can handle such a complex data-based environment. Different research goals such as incorporating expert knowledge in the data-driven models and designing objective functions and uncertainty measures that are easy to learn and to optimize, make this a challenging project and require a mix of applied and theoretical research.

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CWI-SC seminar John Harlim (Penn state)
by Wouter Edeling 17 May '21

17 May '21

Dear all, This Friday (the 21st) at 4PM CET, John Harlim from Penn State will present his work on machine learning of missing dynamical systems at CWI. Here, the model error problem arising from missing dynamics is formulated as a supervised learning task using the Mori-Zwanzig formalism and Taken's embedding theorem. More info can be found in the abstract below. Feel free to share the zoom link if you think the talk may be of interest to your colleagues or students. Kind regards, Wouter Edeling Join Zoom Meeting https://cwi-nl.zoom.us/j/86326565885?pwd=THBiY3BRSUkvVzQ1UjM4Y1RTNGhOZz09 Meeting ID: 863 2656 5885 Passcode: 931873 13 May 2021 16h00: John Harlim (Penn state): Machine learning of missing dynamical systems In the talk, I will discuss a general closure framework to compensate for the model error arising from missing dynamical systems. The proposed framework reformulates the model error problem into a supervised learning task to estimate a very high-dimensional closure model, deduced from the Mori-Zwanzig representation of a projected dynamical system with projection operator chosen based on Takens embedding theory. Besides theoretical convergence, this connection provides a systematic framework for closure modeling using available machine learning algorithms. I will demonstrate numerical results using a kernel-based linear estimator as well as neural network-based nonlinear estimators. If time permits, I will also discuss error bounds and mathematical conditions that allow for the estimated model to reproduce the underlying stationary statistics, such as one-point statistical moments and auto-correlation functions, in the context of learning Ito diffusions.

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PhD in Space-time Neural Memory Models and (Differential) Geometry
by Stratis Gavves 13 May '21

13 May '21

Dear all, Do you know someone interested in Space-time Neural Nets, Memory Models & (Differential) Geometry, Groups, Manifolds? We are looking for an enthusiastic and driven researcher for a Ph.D. application deadline by the beginning of June. For more details on the position, please check the vacancy post: https://www.uva.nl/en/content/vacancies/2021/05/21-326-phd-position-space-t… The position will be supervised by Associate Professor Dr. Efstratios Gavves at the University of Amsterdam, director of QUVA Lab, and director of POP-AART Lab. The position is fully funded by ERC Starting Grant and NWO VIDI. For any questions, you can send an email at egavves(a)uva.nl. Please, share! Best, Stratis

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PhD position in Space-time Neural Memory Models and (Differential) Geometry
by Stratis Gavves 13 May '21

13 May '21

Dear all, Do you know someone interested in Space-time Neural Nets in the context of Generative/Stochastic/Bayesian models? We are looking for an enthusiastic and driven researcher for a Ph.D. application deadline by the beginning of June. For more details on the position, please check the vacancy post: https://www.uva.nl/en/content/vacancies/2021/05/21-325-phd-position-generat… The position will be supervised by Associate Professor Dr. Efstratios Gavves at the University of Amsterdam, director of QUVA Lab, and director of POP-AART Lab. The position is fully funded by ERC Starting Grant and NWO VIDI. For any questions, you can send an email at egavves(a)uva.nl. Please, share! Best, Stratis

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PhD position in Unsupervised Deep Representation Learning for Video
by Stratis Gavves 13 May '21

13 May '21

Dear all, Do you know someone interested in unsupervised deep representation learning for video? We are looking for an enthusiastic and driven researcher for a Ph.D. application deadline by the beginning of June. For more details on the position, please check the vacancy post: https://www.uva.nl/en/content/vacancies/2021/05/21-335-phd-position-unsuper… The position will be supervised by Associate Professor Dr. Efstratios Gavves at the University of Amsterdam, director of QUVA Lab, and director of POP-AART Lab. The position is fully funded by ERC Starting Grant and NWO VIDI. For any questions, you can send an email at egavves(a)uva.nl. Please, share! Best, Stratis

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Machine-learning-nederland