- Machine-learning-nederland

Vacancy postdoc Ghent University
by Stijn Luca 08 Sep '21

08 Sep '21

A vacancy for a postdoc is available at our department (Data Analysis and Mathematical Modelling) of Ghent University. Scientific research is broadly defined and can be about data analysis or mathematical modelling for natural and biological processes applied in one or more research areas at our faculty of bioscience engineering. One possible topic includes experimental design problems within the field of machine learning (e.g. active learning, optimum designs for neural networks, ...) There is a 30% teaching duty in Dutch associated to the position. For more info and the application procedure, click here<https://career012.successfactors.eu/career?company=C0000956575P&career_job_…> Deadline for application is 24/09 [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 Machine Learning seminar: Johanna Ziegel, Fri 10 Sep, 14:00, L016 and zoom
by Wouter Koolen-Wijkstra 01 Sep '21

01 Sep '21

Dear all, It is my pleasure to announce the following CWI Machine Learning seminar. Speaker: Johanna Ziegel (University of Bern) Title: Valid sequential inference on probability forecast performance Date: Friday 10 September, 14:00 Location: CWI L016 and on Zoom (link will follow) This seminar will be held live at reduced capacity, and will be live-streamed on zoom for remote attendance. Please find the abstract below. Hope to see you then. Best wishes, Wouter Details: https://portals.project.cwi.nl/ml-reading-group/events/valid-sequential-inf… ============ Valid sequential inference on probability forecast performance Johanna Ziegel (University of Bern) Probability forecasts for binary events play a central role in many applications. Their quality is commonly assessed with proper scoring rules, which assign forecasts a numerical score such that a correct forecast achieves a minimal expected score. In this paper, we construct e-values for testing the statistical significance of score differences of competing forecasts in sequential settings. E-values have been proposed as an alternative to p-values for hypothesis testing, and they can easily be transformed into conservative p-values by taking the multiplicative inverse. The e-values proposed in this article are valid in finite samples without any assumptions on the data generating processes. They also allow optional stopping, so a forecast user may decide to interrupt evaluation taking into account the available data at any time and still draw statistically valid inference, which is generally not true for classical p-value based tests. In a case study on postprocessing of precipitation forecasts, state-of-the-art forecasts dominance tests and e-values lead to the same conclusions.

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Robotics 2021–2022 Best Video Abstract Award (500 CHF) - [Robotics] (ISSN 2218-6581, citescore 3.5)
by Charlene Dong 24 Aug '21

24 Aug '21

Dear All, A video abstract can help the reader to have a clear understanding of the paper. We are pleased to announce that the Robotics 2021–2022 Best Video Abstract Award is open for applications. Authors are encouraged to provide a video abstract when submitting their manuscripts in order to improve the accessibility of papers. The winner will be selected through a vote by our readers and Award Committee from the papers published in Robotics. Timeline: – Deadline for video abstract submission: 31 December 2022; – Voting period: 1 January 2023–31 January 2023; – Date for announcement of the winner: 28 February 2023. Prize: There will be one awardee for this award, and the winner will receive the following: – CHF 500 (Swiss francs); – A waiver to publish a paper in Robotics (submitted before the end of 2023); – An electronic certificate. Who can participate? Authors who have written articles and reviews published in 2021–2022 with a video abstract. For detail information, please click: https://www.mdpi.com/journal/robotics/awards/1389 PLEASE NOTE: All video abstracts will be assessed for editorial suitability and quality by the editorial team. Please upload your video abstract when submitting your manuscript if you are interested in the award. For the authors of published papers, please send a video and a manuscript link to the Robotics Editorial Office (robotics(a)mdpi.com) if you would like to apply for the award. Charlene Dong Managing Editor Email: charlene.dong(a)mdpi.com Robotics (http://www.mdpi.com/journal/robotics/) 10th Anniversary of Robotics: A series of special content and events at https://www.mdpi.com/journal/robotics/anniversary

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[jobs] PostDoc@TUDelft - Deep Learning for Human-Robot Interaction
by Jens Kober - 3ME 03 Aug '21

03 Aug '21

Dear colleagues, Apologies for cross-posting. We have an opening for one 18 months postdoc position "Deep Learning for Human-Robot Interaction" at TU Delft, Netherlands. This project will focus on various aspects of human robot interaction leveraging deep learning methods. Robots can learn to interact with humans but also from interactions with humans. In both cases understanding human behavior is crucial. Human state and intentions are very ambiguous and uncertain, fusing information from multiple sensing and input modalities might allow the robot to disambiguate. For example, in imitation learning approaches instructions from one modality typically are not complete. Here the challenge lies in learning which information, from which modality is relevant for the task, and which is not. This vacancy is part of the project Open Deep Learning Toolkit for Robotics (OpenDR) https://opendr.eu/ . For more information and instructions on how to apply (deadline September 15th), see https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?… Best regards, Jens -- Dr.-Ing. Jens Kober Associate Professor, Cognitive Robotics department Delft University of Technology Mekelweg 2 (3mE Building, Office F-2-380) 2628 CD Delft, The Netherlands Tel.: +31 (0)15 27 85150 http://www.jenskober.de

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6 PhD and 1 Postdoc Positions in Machine Learning (University of Amsterdam, Delft University of Technology)
by Tim van Erven 21 Jul '21

21 Jul '21

Forwarding on behalf of Joris Mooij: We are seeking six PhD candidates and a postdoctoral researcher for the recently established Mercury Machine Learning Lab (MMLL). In this lab, researchers from the University of Amsterdam (UvA) and Delft University of Technology (TU Delft) will be working together with data scientists from Booking.com to develop the machine learning foundations for a new generation of recommendation systems. Motivated by real-world problems faced in industry, we will investigate fundamental problems regarding generalization and bias removal in data analysis, and develop more usable machine learning and reinforcement learning techniques. As part of the MMLL initiative, the University of Amsterdam and the Delft University of Technology are inviting applications for the following six fully funded PhD positions: Position 1: Bridging online and offline evaluation (supervisor: Maarten de Rijke, UvA) Position 2: Avoiding filter bubbles by correcting for selection bias (supervisor: Joris Mooij, UvA) Position 3: Domain generalization and domain adaptation (supervisor: Joris Mooij, UvA) Position 4: Training NLP Models for Better Generalisation (supervisors: Ivan Titov & Wilker Aziz, UvA) Position 5: Model-based Exploration (supervisors: Matthijs Spaan & Frans Oliehoek, TU Delft) Position 6: Parallel Model-based Reinforcement Learning (supervisors: Matthijs Spaan & Frans Oliehoek, TU Delft) More details on the contents of the MMLL research projects are provided on the MMLL webpage, https://icai.ai/mercury-machine-learning-lab/ In addition, the University of Amsterdam invites applications for a postdoctoral researcher to assist with the research and the supervision of the PhD students. For further information, see the official job advertisements at: https://www.uva.nl/en/content/vacancies/2021/07/21-579-phd-4-and-postdoc-1-… https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?… The application deadline is August 23rd, 2021. ------------------------------------------------------------- Joris Mooij Professor in Mathematical Statistics University of Amsterdam http://www.jorismooij.nl/

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Two funded PhD positions on Reinforcement Learning in the Real World at TU Delft
by Frans Oliehoek 21 Jul '21

21 Jul '21

(apologies for cross-posting) Do you have what it takes to push reinforcement learning beyond the realm of games? TU Delft (Netherlands) offers 2 PhD positions focusing on reinforcement learning in the recently established Mercury Machine Learning Lab (MMLL). In this lab, researchers from the University of Amsterdam and Delft University of Technology will be working together with data scientists from Booking.com to develop more usable reinforcement learning and other machine learning techniques. Reinforcement learning is a promising approach to learn to control decision making problems that extend over time, but so far applications have been largely limited to synthetic settings such as games. Motivated by real-world problems faced in industry, we will investigate fundamental problems in reinforcement learning. For instance, we will study effective exploration in non-stationary environments and learning using many parallel trials. The candidates will be jointly supervised by Dr. M.T.J. Spaan and Dr. F.A. Oliehoek. The MMLL collaboration provides the unique opportunity to test AI techniques in the real world, allowing new machine learning methods to be safely developed for wide application, for example in mobility, energy or healthcare. In addition to the existing researchers, the Mercury Machine Learning Lab will comprise six PhD candidates and two postdocs who will work on six different projects related to bias and generalisation problems over the course of the next five years. Further details and an application form can be found via the following links. https://icai.ai/mercury-machine-learning-lab/ https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details?… Application deadline for full consideration: 23 August 2021 Informal enquiries: Matthijs Spaan (m.t.j.spaan(a)tudelft.nl) and Frans Oliehoek (f.a.oliehoek(a)tudelft.nl).

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CWI-SC seminar Ilias Bilionis (Purdue U)
by Wouter Edeling 15 Jul '21

15 Jul '21

Dear all, On Thursday the 22nd of July, at 3PM CET, we will have the last speaker in our seminar before the summer stop. Ilias Bilionis from the School of Mechanical Engineering at Purdue University will talk about physics-informed neural networks, high-dimensional uncertainty quantification, automated discovery of physical laws and complex planning, in the context of work done at NASA's Resilient Extra-Terrestrial Habitats Institute. You can find the zoom link and abstract below. Feel free to share the zoom link with anyone that might be interested. Kind regards, Wouter Edeling 22 Jul. 2021 15h00 CET: Ilias Bilionis (School of Mechanical Engineering, Purdue University): Situational awareness in extraterrestrial habitats: Open challenges, potential applications of physics-informed neural networks, and limitations I will start with an overview of the research activities carried out by the Predictive Science Laboratory (PSL) at Purdue. In particular, I will use our work at the Resilient Extra-Terrestrial Habitats Institute (NASA) to motivate the need for physics-informed neural networks (PINNs) for high-dimensional uncertainty quantification (UQ), automated discovery of physical laws, and complex planning. The current state of these three problems ranges from manageable to challenging to open, respectively. The rest of the talk will focus on PINNs for high-dimensional UQ and, in particular, on stochastic PDEs. I will argue that for such problems, the squared integrated residual is not always the right choice. Using a stochastic elliptic PDE, I will derive a suitable variational loss function by extending the Dirichlet principle. This loss function exhibits (in the appropriate Hilbert space) a unique minimum that provably solves the desired stochastic PDE. Then, I will show how one can parameterize the solution using DNNs and construct a stochastic gradient descent algorithm that converges. Subsequently, I will present numerical evidence illustrating this approach's benefits to the squared integrated residual, and I will highlight its capabilities and limitations, including some of the remaining open problems. Join Zoom Meeting https://cwi-nl.zoom.us/j/85892880510?pwd=QS9vaUxDWHBjWVp1djdIaVZvSmhUZz09 Meeting ID: 858 9288 0510 Passcode: 480369

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[jobs] Associate professor position at UU
by Wouter Koolen-Wijkstra 05 Jul '21

05 Jul '21

Dear all, I'm forwarding on behalf of a contact at KLM the following interesting position: Utrecht University has an vacancy for an associate professor on Algorithms, Optimization and/or AI in Aviation in collaboration with KLM. https://www.uu.nl/en/organisation/working-at-utrecht-university/jobs/associ… The application deadline is 15 August 2021. Cheers, Wouter

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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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Machine-learning-nederland