Open positions
Research fellow to lead and coordinate research efforts on Human-AI collaboration

DesCartes Program

DesCartes is a 5-year  program that aims to develop hybrid artificial intelligence systems that combine learning from data, prior knowledge, and logical reasoning for smart city applications, such as digital energy, infrastructure monitoring, and air traffic control.  The program brings together 80 permanent researchers from France and Singapore, with the support of large industrial groups, including Thales SG, EDF SG, ESI Group, CETIM Matcor, and ARIA. The research takes place mainly in Singapore, at the premises of CNRS@CREATE on the campus of the National University of Singapore (NUS).

Description

The program has an open position for a postdoctoral research fellow to lead and coordinate research efforts on human-AI collaboration, specifically on how AI can assist humans in decision-making and how humans can complement AI in critical decisions. The research fellow will work with researchers from the French National Centre for Scientific Research (CNRS), the Agency for Science, Technology, and Research (A*STAR), and the National University of Singapore (NUS) to develop state-of-the-art methods in human-in-the-loop machine learning, human-AI interaction, and human-centric AI.

Experience & Qualifications

Applicants must minimally have a PhD in Computing, Engineering or a related discipline, research experience in AI and/or human-computer interaction, a strong publication track record

Further information & Contact

The salary for the position ranges from 70K to 85K SGD per year, depending on suitability and experience

Workplace: CREATE Tower, Create Way #08-01, Singapore 138602

Interested applicants should email a cover letter and CV to the lead PIs of Work Package 4 of DesCartes,  Wei Tsang Ooi (ooiwt@comp.nus.edu.sg) and Christophe Jouffrais (christophe.jouffrais@cnrs.fr).

Research Assistant in Drone Companion for People with Visual Impairments

Job Description

IPAL invites applications for the position of Research Assistant in the Department of Computer Science, School of Computing (SoC). SoC is strongly committed to research excellence in all its dimensions: Searching for fundamental results and insights, developing novel computational solutions to a wide range of applications, building large-scale experimental systems, and improving the well-being of society. We seek to play an active role both internationally and locally in the core and emerging areas of Computer Science and Information Systems.

The Research Assistant will be responsible for working closely with the Principal Investigator (A/Prof Ooi Wei Tsang) and co-Principal Investigators (A/Prof Suranga Nanayakkara & Professor Christophe Jouffrais) on developing a drone companion for People with Visual Impairments. We envision a lightweight, personal drone, equipped with state-of-the-art computer vision algorithms, that serves as a highly-customizable assistive device for a PVI.

Job responsibilities would include the development of drone applications, conducting user studies, and documenting the research.

Qualifications

The potential candidate should possess experience or interest in the following areas:

  • At least a bachelor’s degree in computer science, cognitive science or related field with a focus on Software development
  • Hands-on experience in full stack development
  • Experience in areas such as Human-Computer Interfaces, User-Centred Design, and Computer Vision would be preferred
  • Excellent communication skills (written and verbal) and interpersonal skills

How to Apply:

Email CV and a short statement of interest to Prof Christophe Jouffrais Cristophe.Jouffrais@cnrs.fr and A/Prof Ooi Wei Tsang ooiwt@comp.nus.edu.sg

Postdoctoral fellow in computational biology, bioinformatician, internship opportunities in Research Data Integration Group, Biomedical Datahub Division, Bioinformatics Institute (A*STAR)

Supervisor: Dr. Woo Xing Yi, Senior Principal Investigator and Head of Research Data Integration
Contact: woo_xing_yi@bii.a-star.edu.sg

 

About us
Data science is an important component of biomedical and translational research, where
data of multiple modalities are being constantly generated at unprecedented scale. The
Research Data Integration group in the Biomedical Datahub Division of the Bioinformatics
Institute (BII), A*STAR, aims to bridge the complexity of computational biology and data
science with the needs of biologists and clinicians to drive biological discoveries and predict
translational outcomes. One of our immediate challenges is to integrate and analyze multi-
omics, imaging and clinical data generated by biomedical institutes, hospitals and national
initiatives to improve the usability and interpretability of large-scale multimodal datasets of
cancer and other diseases. We seek motivated individuals to join us to push the potential of
biomedical data in truly benefitting patients.

 

Project description
We work closely with clinicians to explore personalized treatment options for cancer
patients using multi-omic and spatial profiling, and therapeutic screening in patient-derived
models. Data of multiple modalities are generated in the process, and we are developing
systematic workflows to integrate and analyze the data to enable clinical-decision-making,
predict translation outcomes and drive biological discoveries. This project is looking for
candidates to develop computational methods, including big-data analytics and AI/ML
approaches, to analyze and integrate the multi-modal data (sequencing, imaging, spatial
profiling, treatment response and clinical data) that can deliver translational outcomes to
cancer patients. The candidate will have the opportunity to work in a multi-disciplinary team
led by a senior Principal Investigator highly experienced in cancer computational biology and
clinician-scientists specializing in oncology. Eventually, the candidate will receive training in
both computational biology and translation oncology disciplines.

 

The candidate is expected to work on any (but not limited) to any of these tasks, depending on position, experience, field of study and interests.
1. Develop, implement and benchmark executable workflows for variant (SNP, Indels, SV, CNV) calling from WES/WGS data and transcriptome profiling from RNASeq data.
2. Develop image processing workflows for histology images using AI/ML and computer vision methods.
3. Write scripts to output data in a format that can be integrated with publicly available cancer datasets.
4. Organize and analyze publicly available cancer datasets, including sequencing and drug treatment.
5. Develop visualization tools to visualize results in a meaningful way.
6. Organize all data in a structured manner using relational databases.
7. Develop methods to integrate multi-modal data.

8. Curation of cancer treatment and biomarkers, and patient clinical data.
9. Develop cancer variant annotation database

 

Requirements
• The candidate should have basic programming skills (e.g. Python, R, RStudio, Jupyter Notebook, RShiny, SQL), except for curation tasks.
• Familiarity with Unix/Linux environment or cloud architecture would be an advantage
• Strong analytical and problem-solving skills.
• Excellent oral and written communication and presentation skills.
• Able to work independently, and as part of a team

 

Suitable field of study
Bioinformatics, Computational biology, Computer science, Data science and analytics, Statistics and biostatistics, Mathematics, Genetics, Life sciences, Biology, Epidemiology, computer vision, any field of Science and Engineering, Pharmacy, Medicine

Research Fellow (postdoctoral) position is available immediately in the Computational Digital Pathology Lab (CDPL) of Bioinformatic Institute (BII), A*STAR, Singapore.

Novel imaging analysis methods and the advancement of Artificial Intelligence are making it feasible to extract quantitative information from Digital Pathology images. New knowledge, skills, and approaches are urgently required to develop novel, efficient and reliable computational tools for emerging challenges in biological and biomedical studies. Our CDPL
is dedicated to the development of new solutions in this field. Currently, we are expanding our team and seeking enthusiastic scientists to join us!
For a research overview of our unit, please refer to https://www.a-star.edu.sg/bii/research/ciid/cdpl
General requirements:
• Self-motivated scientist/Ph. D graduate to pursue a scientific career. Independent and passionate about biological image processing projects;
• Good team player. Able to undertake independent research projects under the direction of the PI together with other team members;
• Hold a Ph.D. in a relevant field of computer vision, image processing or machine vision, or other relevant fields;
• Good general knowledge and concept of science and engineering;
• Excellent scientific/technical writing skills and communication capability;
• Prior experience in working with image analysis projects (Industrial or academic).

Specific technical requirements:
• Excellent experience, knowledge, and skills in one or two following programming languages, i.e., Matlab, C/C++, Java or Python;
• Excellent knowledge and skills in digital image processing;
• Deep understand of AI learning, machine learning and data science with hand-on skill and experience.
• Skills for numerical computational algorithms; Strong in mathematics theories.
• Rigid and logical thinking of scientific problems;
• General understanding of machine learning, pattern recognition, and artificial intelligence is a plus, but not compulsory;
• Basic knowledge of cell biology or pathology is a value-add;
• Present research achievements at internal/external seminars and conferences

Please email a detailed CV containing a list of publications to Dr. Weimiao YU,
yu_weimiao@bii.a-star.edu.sg
We regret that only shortlisted applicants will be notified.

Research Associate (M.E. or M.Sc.) position is available immediately at the Computational Digital Pathology Lab (CDPL) of Bioinformatics Institute (BII), A*STAR, Singapore.

Our CMPL team is dedicated to the development of new solutions in this field. Currently, we are expanding our team and seeking enthusiastic young researchers to join us!
For a research overview of our unit, please refer to https://www.a-star.edu.sg/imcb/imcb-research/scientific-programmes/innovative-technologies

General requirements:
• Self-motivated researcher/M.E. or M.Sc. graduate to pursue a scientific career. Independent and passionate about biological/biomedical imaging projects;
• Good team player. Able to undertake independent research projects under the direction of the PI together with other lab members
• Hold a Master degree in a relevant field;
• Good general knowledge and concept of science and engineering;
• Excellent scientific/technical writing skills and communication capability
• Prior experience in working with imaging projects, such IHC imaging, H&E imaging and Multiplex immunofluorescence imaging (Industrial or academic).

Specific technical requirements:
• Excellent experience, knowledge, and skills in one or two following programming languages, i.e., Matlab, C/C++, Java or Python;
• Good experience on image quality control;
• Excellent knowledge and skills in medical sample staining and imaging technical;
• Rigid and logical thinking of scientific problems;
• General understanding of machine learning, pattern recognition, and artificial intelligence is a plus, but not compulsory;
• Basic knowledge of cell biology or pathology is a value-add;
• Present research achievements at internal/external seminars and conferences

Please email a detailed CV containing a list of publications to Dr. Weimiao YU, wmyu@imcb.a-star.edu.sg
We regret that only shortlisted applicants will be notified.

Opening for Bioinformaticians in Genomics and Sequences Analysis for the Eisenhaber Group (GIS/BII Singapore)

The Eisenhaber Group affiliated with the Genome Institute of Singapore (GIS) and Bioinformatics Institute (BII) of A*STAR has two openings for researchers/bioinformatics analysts in omics data (with emphasis on protein sequence data) analysis (Equal Opportunity/Affirmative Action/Equal Access Employer). The application of bioinformatics, computational and advanced data analysis techniques and concepts is aimed at discovering biomolecular mechanisms that are relevant for
phenotypic effects, disease, natural product research, etc. The successful applicant will also be involved in mentoring interns and students helping them getting an entrance into the field. An introduction into the group’s in-house software suites and local databases as well into advanced concepts of protein sequence analysis will be provided.

Job Duties
• Analyze multi-modal omics datasets using available open software packages and tools and, potentially, own programs and scripts with the goal of discovering hints for biomolecular mechanisms. Interpret results in biological terms.

• Reformat data sets so that they can serve as input for different software suites. Map various datasets onto a common basis so that integrated analysis becomes possible. Assess data quality and clean up data sets.
• Analyze in-house data in context with public datasets. The analysis will be carried out jointly with collaborators from experimental life science and/or clinical labs.
• Analyze life science literature (e.g., PMID 30265449)
• Assist with the preparation/writing of scientific manuscripts and grant submissions.

Preferred Qualifications
• A PhD or MSc in Computer Science/Engineering, Bioinformatics, Genetics, Biology, Bioinformatics, Biostatistics, Computational Biology or Computer Science (a MSc applicant might move towards a PhD)
• Preferentially with solid biological background knowledge
• Preferentially some ability in programming and/or scripting (e.g. Python/R programming), some knowledge of standard bioinformatics analysis tools for omics data, pathway analysis, etc. Familiarity with Unix/Linux is desirable.
• New tools appear all the time and learning is part of the job though some experience will be helpful.

If interested, please send your application letter, updated CV, supporting documents/certificates/work examples/thesis/etc. to franke@bii.a-star.edu.sg.

Positions open to both interns and PhDs

All the following positions are open for both interns and PhDs candidates. If you want to inquire or apply, please send your CV to the names indicated at the end of each proposal.

A regular PhD stipend with IPAL is ~1400€ or 2700 $SG. But under certain conditions (e.g. nationality), IPAL can allow you to apply for exceptional thesis stipends from 4000 to 5500 SGD. Feel free to inquire

Internships and PhD positions in the framework of the Descartes Program

All the following positions are open for Interns, and can be continued by a PhD. All these positions will take place in the exciting scientific environment of the Descartes collaborative program. See here for more in Descartes: Descartes presentation

Starting date

The starting date will be in early 2022. All the master internships can lead to a PhD in France or Singapore. Interns who aim to do a PhD will be preferred.

Application

Send an email to the corresponding supervisors with the following documents: 

  • Complete CV (with possible publications)
  • Letter of motivation
  • transcripts of records since L1 or Prepa
  • Report of a previous internship

You may apply to more than one proposal. In this case, please send the documents to all supervisors and mention it in your message.

Proposal #1 - Understanding the environment from drones with multiples sensors (closed)

Supervisors: Lai-Xing Ng (Contact: ng_lai_xing@i2r.a-star.edu.sg) and Benoit Cottereau (Contact: benoit.cottereau@cnrs.fr)

Abstract: Drones, or machines in general, have a multitude of sensors that provide information about the surroundings. Existing works on drone perception often use image-based sensors, such as RGB cameras and depth cameras. Image-based sensors are susceptible to motion blur as well as variation in illumination and thus do not work well when the drone is fast-moving. For teleoperated drones, the human operator can only rely on the live video feed of a single camera and the restricted field-of-view affects the human understanding of the drone’s environment. In this project, the aim is to utilize the available sensors and provide a human operator a perspective of being at the drone’s location. Selected candidate will work on developing novel approaches on how distributed sensors can communicate, collaborate (including changing what they sense) and process the signals in an energy-efficient way to extract meaningful information from the scene, in response to existing knowledge models (long term memory) and real-time interaction and decisions from humans, and send back the information to humans for visualization. Research tasks include:

  • Process different types of sensory signals (e.g. data collected from event-based cameras, synchronous cameras, and other sensors) for scene understanding (e.g. object detection and localization) using neuromorphic systems based on artificial neural networks and embedded on a single or multiple drones.
  • Extract meaningful information (3D layout of the scene, objects of interest, threats, etc.) and combine with existing knowledge models.
  • Provide meaningful multimodal feedback to the user based on a wearable device (e.g. smart glasses) that should provide remote (augmented) perception.

Expected skills: The candidate should be willing to work in an international environment which involves Singapore and France, have a good level in English and very good programming skills (in Matlab, Python or C++).

Proposal #2 - Drone Piloting from Different Perspectives (closed)

Supervisors: Shen ZHAO (zhaosd@comp.nus.edu.sg) and Christophe JOUFFRAIS (Christophe.Jouffrais@cnrs.fr)

Abstract: New technologies such as mixed reality, natural or wearable interfaces, as well as Artificial Intelligence are beginning to take hold in production facilities. They promise performance gains but can also improve safety and comfort in interactions between human operators and semi-autonomous systems. For these technologies to be accepted and deployed, human factors must be considered.

In this internship, we will design and evaluate a multisensory interface for drone piloting from different perspectives. The aim of the project will be to define the characteristics of a multimodal interface for the control of semi-autonomous drones. Behavioral experiments and the analysis of the collected data will allow the selection of the most suitable parameters for both the design of the interfaces and the evaluation of the human-system interaction. 

Expected skills: The candidate must have skills in human-computer interaction, cognitive science and/or human factors. He-She should be willing to work in an international environment which involves Singapore and France, have a good level in English and good programming skills.

Proposal #3 - Future Video Prediction using Generative Models

Supervisors: Ying SUN (suny@i2r.a-star.edu.sg) and Christophe JOUFFRAIS (Christophe.Jouffrais@cnrs.fr)

AbstractLearning to predict the future is an important research problem in machine learning and artificial intelligence. In this project, we focus on the task of predicting future frames in videos, i.e., video prediction, given a sequence of previous frames. Recently, deep-learning-based methods have emerged as a promising approach for video prediction, especially generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs). VAEs can generate various plausible outcomes, however, the predicted frames are blurry and of low quality. While GAN-based models tend to produce higher quality future frames, adversarial training is unstable and may lead to model collapse. Therefore, we will explore state-of-the-art generative models for video prediction and develop new strategies to address the limitations of existing methods.

Expected skills: The candidate should be willing to work in an international environment which involves Singapore and France, have a good level in English and very good programming skills (in Matlab, Python or C++).

Proposal #4 - human-in-the-loop learning

Supervisors: Lai-Xing Ng (Contact: ng_lai_xing@i2r.a-star.edu.sg), Wei-Tsang Ooi (Contact: ooiwt@comp.nus.edu.sg)  and Axel Carlier (Contact: Axel.Carlier@toulouse-inp.fr)

AbstractWhile deep learning has brought important advances in many domains, large labeled datasets are required to ensure good model performances. Several models for collaborations between human and machine learning have been proposed to overcome this limitation and try to decrease the need for labeled data. In active learning, the model explicitly chooses data samples for humans to label, which is then fed into the training process in an online fashion. 

Unlike learning from a large number of pre-labeled data samples, human inputs in human-in-the-loop learning have a larger impact or even overriding effects on machine decisions. Such human-AI collaboration models make it possible for malicious humans to impact the outcome of machine learning models.

In this internship, we plan to build upon existing criteria, such as for example expected model output change (EMOC), to study possible trade-off between impact of (possible malicious) input vs. how fast a model can learn from humans.

Proposal #5 - Interactive Explainable AI

Supervisors: Christophe Hurter (christophe.hurter@enac.fr), Brian Lim (brianlim@comp.nus.edu.sg), and Jamie Ng (jamie@i2r.a-star.edu.sg)

AbstractWith AI capabilities, drones can be used to automatically inspect airplanes and buildings to improve the safety of these structures. However, it is ultimately dependent on the human operator to verify the severity of defects. In this project, we will develop interactive methods to make AI explainable for drone operators and inspectors to interpret and verify the image predictions. This research will investigate how to support user understanding of AI decisions using interactive visualization, and explainable AI. 

We are looking for talented candidates to join our multidisciplinary team.  The project looks into how robotics, computer vision, artificial intelligence, virtual/augmented reality and human-computer interaction can lead to effective human-AI collaboration. We are looking for candidates with passion in research and development, as well as in translating R&D technology into industry applications. 

Expected Skills:

  • Qualification/Field of Study: Bachelors or Masters
  • Technical skills: Strong programming skills (e.g., Python, Javascript, C++).
  • Experience and Knowledge: Computer Vision and Machine Learning, Virtual Reality, Human-Computer Interaction, etc.
  • Aptitude: Critical thinker, self-motivated, can work both independently and in teams, with good analytical and communication skills.

 

Previous Internship Positions

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