Machine-learning-nederland July 2021

machine-learning-nederland@list.uva.nl

4 participants
5 discussions

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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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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Machine-learning-nederland July 2021