Open positions

Open positions with IPAL

  • PhD position – Can GPS guidance lead to cognitive mapping?

Read the PhD position here

  • 2020  Master Internship positions:

Multimodal feedback in a virtual scene

Designing non-visual multimodal feedback to help with navigation in a virtual scene. The student will be helping to develop audio and tactile feedback to guide a user in navigation through a virtual scene. These two modalities will be integrated in a system that attempts to understand user preferences, obtain their feedback for human-in-the-loop reinforcement learning and evaluate our approach. The job task involves help to prepare a software library that, given a 3D virtual scene and a route, renders the orientations and directions to the users using audio (via text-to-speech) and tactile feedback. Both signals will be sent to an earphone and one or two wrist-based tactile bands with motors to provide spatial cues.

Date: project start between May and Dec 2020

Contact: C. Jouffrais Christophe.jouffrais@ipal.cnrs.fr and Shen Zhao dcszs@nus.edu.sg

Easier Scene Understanding with Deep Learning using Context

In this project, the intern will study the problem of scene understanding from a given image using a deep neural network.  Current state-of-the-art methods require a complex and deep network and a large amount of training data.  We will explore how having prior context information about the scene can simplify the problem, and thus the complexity of the network as well as the amount of training data required.

The intern will assist the researcher in experimenting with different neural network models and how the context information can be integrated into the training and inferencing phase of the problem.

Date: project start between May and Dec 2020

Contact: Axel Carlier Axel.Carlier@enseeiht.fr and Wei Tsang Ooi ooiwt@comp.nus.edu.sg

How To Fool a Deep Neural Net with another Deep Neural Net

Deep neural network has been proved successful in computer vision and natural language. Nevertheless, the research literature has shown that they can be vulnerable if we change several pixels of an image of a dog so that the model may make a wrong prediction. Such a mutated image is called an adversarial sample for the neural network. Such perturbation-based approach looks for adversarial samples from a low and detailed perspective. In this research, we investigate a new adversarial sample generation technique by exploring GAN (Generative Adversarial Network). We are exploring how to use GAN to generate adversarial samples from a higher perspective. More specifically, we are exploring to generate a face of Bob which has never appeared in the training set but can be mistakenly classified as Alice to fool some face recognition system.

Date: project start between May and Dec 2020

Contact: Blaise Genest blaise.genest@irisa.fr and Jin Song Dong dcsdjs@nus.edu.sg 

Neural Network for Differential Equations

Differential equations are one of the main tools for the modelling, simulation and analysis of complex systems in most domains of science and engineering. Neural networks have recently been shown to be able to effectively and efficiently solve differential equations. In fact, several possible approaches are still under investigation. In this project, the researcher will implement and evaluate several existing and new approaches to represent and solve systems of differential equations with neural networks. The student researcher may also be involved in the development of applications of the work to hydrology, meteorology and climate change.

Date: project start between May and Dec 2020

Contact: Talel Abdessalem Talel.Abdessalem@telecom-paristech.fr and Stephane Bressan steph@nus.edu.sg

 

Previous positions:

      2019 Internships:

      • Towards ageing-well through trusted intelligent systems based on AI, IoT and Formal Analysis
      • Android development of urban mobility app using Fitbit and environment APIs (app implementation, analysis, reasoning)
      • Front and back-end dev and data analysis Node.js (Machine learning, IoT for health, on site validation)
      • Software IoT architecture (refactoring, optimization of platform to enhance large-scale deployments)
      • Web-based visualisation of GeoJSON (interactions in WebGIS environment)
      • Continuous and nonintrusive vital sign monitoring using optical fibre sleep mat (machine learning, sleep cycles data analysis) in collaboration with Khoo Teck Puat Hospital (KTPH) and Singapore University of Technology and Design (SUTD)

      Get to know more:
      Scientists worldwide are welcome to join our challenges! IPAL provides great opportunities to researchers and students from all nationalities who desire to blossom in an excellent international research laboratory. We are committed to provide a unique platform for candidates to begin research and develop their skills in a top-ranked university fully supported by distinguished and world-renowned researchers from Singapore and France.
      CNRS and Universities mobility: If you are already a researcher working for the CNRS, we will be very honored to welcome you in our laboratory. Please have a look at the CNRS website for the procedure, do come in touch with us to prepare a joint ambitious projects, able to boost your carreer, and do not hesitate to contact us for further assistance: CNRS Mobility website
      Singapore, a high-tech and world-class scientific environmentIn a very competitive scientific environment, surrounded by dynamic and talented scientists and supported by one of the best basic and translational research infrastructures worldwide, working in Singapore is a valuable experience. In partnership with the National University of Singapore and the Agency for Science, Technology and Research institutes, world-class scientists from all major scientific centres in the world, are exchanging and sharing with us all year long, generating a prolific scientific osmosis.
      Open PhD Positions and Regular PhD applications: In order to work with IPAL, you need to come in touch with one of our staff during your first year. Please look at our research goalsaxesprojects and publications and you will quickly understand what competencies we will always welcome. Don’t hesitate to contact us if needed. Beside the open position(s) above, a regular submission can be done via the graduates portal NUS, School of Computing, Computer Science Dpt. or NUS, Faculty of Engineering depending on your profile. See also the PhD Programme at NUS School of Computing. Another possibility to get a NUS degree at IPAL is to go for the SINGA – Singapore International Graduate Award programme or the ARAP – A*STAR Research Attachment Programme both funded by A*STAR, with a graduation through NUS in the case of IPAL. Last but not least, regular applications can be done via the EDITE doctoral scool (Informatics, Telecommunications and Electronics) of the University Pierre and Marie Curie, Paris, France or the Doctoral School for Computer Sciences, Applied and Pure Mathematics (MSTII) of the University Joseph Fourier, Grenoble 1, France, for a French PhD while working at IPAL in Singapore or in a collaborative way with highly reputable CNRS labs in France. Please get in touch with us to define your project before application in this case.

      Previous positions:

      2017 Support Team

      2017 Master Internship Proposals

      Internships hosted by our partners on joint projects:

      2017 Post-doc fellowship @BII:

      2016 Master Internship Proposals

      Internships hosted by our partners on joint projects:

      2016 PhD positions

      2014 PhD positions

      2015 Master internship positions

      Internships hosted by our partners on joint projects:

      2014 Master internship positions