Total 3 Education / Training job vacancies

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Assistant-%28Multi-Agent-Strategy-Learning%29/33156-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

This position involves working on a project related to the coordination of large teams of autonomous robots (e.g., UAVs) in mixed cooperative-competitive environments. The Research Assistant will support the development, simulation, testing, and implementation of collaborative control algorithms for multi-agent robotic systems, under the guidance of the PI and senior researchers. The work will first focus on simulation studies and may later involve hardware experiments with project partners. The role provides an opportunity to work with the PI’s team at the National University of Singapore, as well as industrial, government, and overseas collaborators. The successful candidate is expected to be motivated, responsible, and willing to learn, with a Master’s-level background in robotics, control, computer science, electrical engineering, mechanical engineering, or related disciplines. Prior exposure to multi-agent systems, robotic control, reinforcement learning, or autonomous aerial vehicles would be an advantage.

The main research tasks for the project include, but are not limited to:

• Assisting in the development and evaluation of conventional and learning-based controllers for collaborative control of multi-agent robotic systems in mixed cooperative-competitive environments.
• Implementing and testing control algorithms in simulation environments, with possible support for hardware experiments on robotic or UAV platforms.

Qualifications

• Good programming skills in Python.
• Basic experience with PyTorch, deep learning, reinforcement learning, or related machine learning tools.
• Basic understanding of robotics, control systems, machine learning, or multi-agent systems.
• Experience with robotic simulation environments or aerial simulators would be an advantage.
• Prior exposure to UAVs, autonomous robots, or hardware experiments would be beneficial but is not required.• Ability to read research papers and summarize key technical ideas.
• Good written and spoken communication skills.
• Ability to work independently on assigned tasks while collaborating effectively within a research team.

Job profile:

Research Assistant - HumanSciences Methodology for Agent-Based Modelling

DesCartes Programme WP1× WP6

Context

The DesCartes Programme, led by CNRS@CREATE in Singapore, brings together AI, engineering and human sciences to design trustworthy hybrid AI systems. In partnership with the National University Health System (NUHS), WP1 (Agent-Based Modelling) and WP6 (Ethics of Autonomy and Care in AI Decision-Making) are jointly developing an agent-based model (ABM) to support the redesign of selected NUH services. Grounding this model in empirically rich descriptions of care practices, patient trajectories and staff routines requires dedicated field-based human sciences work.

Mission

Working at the interface between WP1 and WP6, the Research Assistant will contribute to the design and implementation of human sciences methodology feeding into the ABM.Main tasks:

- Design and conduct field observations.

- Prepare and carry out semi-structured interviews with healthcare staff, patients and caregivers; transcribe and analyse the material (thematic/qualitative analysis).

- Translate qualitative findings into structured inputs for the ABM in close collaboration with the WP1 modelling team.

- Participate in joint WP1-WP6 meetings, internal reports, and co-author scientific outputs.

Profile

- Master's degree (or equivalent) in psychology, sociology, anthropology, public health, human factors, cognitive science or related field.

- Solid training in qualitative methods.

- Interest in interdisciplinary work with computer scientists/modellers; basic familiarity with ABM or simulation approaches is a strong asset.

- Experience in clinical / hospital environments is a plus.

- Excellent working English; additional languages spoken in Singapore (Mandarin, Malay, Tamil) are appreciated.

- Autonomy, rigour, strong interpersonal and writing skills.

Practical Information

- Location: Singapore, CREATE Tower,

- Contract type & duration: 6 months, possibility of extension

- Start date: mid-July

- Salary: range, SGD5,500 - SGD7,200

- Supervision: Prof. Bahia Guellai (WP6) and Blaise Genest (WP1)

Application

Please send a CV, a short cover letter and the contact details of two referees to bahia.guellai@cnrsatcreate.sg, with the subject line RA - WP1×WP6. Applications will be reviewed on a rolling basis until the job is filled.

Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.

NUS Career Portal link: https://careers.nus.edu.sg/job/Research-Fellow-%28Multi-Agent-Strategy-Learning%29/32346-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

This position involves working on a project related to the coordination of large teams of autonomous robots (e.g., UAVs) in mixed cooperative-competitive environments. The objective of this position includes contributing to model development, solution, and implementation of such collaborative controllers on actual platforms, first in simulation and then on hardware. The role involves the opportunity to work with the PI’s team within the National University of Singapore, as well as a large industrial partner and government agency, plus overseas collaborators overseas. The successful candidate will be self-motivated with an outstanding track record in robotics, control, computer science, or related disciplines, with a special focus for multi-agent/-robot systems. The main research tasks for the project include but will not be limited to:

•             Developing robust conventional and learning-based controllers for collaborative control of multi-agent robotic systems in mixed cooperative-competitive environments

•             Designing and implementing controllers in simulation and then on hardware with our partners.

Qualifications

•             Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.)

•             Literature review/summarizing skills

•             Simulations abilities (especially aerial simulators)

•             Experience with implementation of deep learning model on hardware (in particular, autonomous aerial vehicles)

•             Experience publishing papers, and supervising undergraduate/master’s students

•             Excellent writing/spoken communication skills