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Research Fellow (Battery Venting Gas Combustion Simulations)

Fresher
SGD 5,750 - 11,000 per month
  • Posted 12 hours ago
  • Be among the first 10 applicants

Job Description

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-%28Battery-Venting-Gas-Combustion-Simulations%29/34232-en_GB/

We regret that only shortlisted candidates will be notified.

Job Description

The Department of Mechanical Engineering at the National University of Singapore (NUS) invites applications for a Postdoctoral Research Fellow to conduct research in computational combustion and reacting-flow modelling. This is a one-year research appointment, with the possibility of extension subject to satisfactory research progress and funding availability. The successful candidate will work closely with the Principal Investigator and research team and will have opportunities to collaborate with leading academic or industrial partners.

Key Responsibilities

. Develop and conduct advanced CFD (such as LES or DNS) and combustion modelling of reacting flows and combustion systems.

. Develop and apply combustion chemistry, turbulence-chemistry interaction, and relevant numerical models.

. Perform simulations using OpenFOAM, or other relevant computational tools, including HPC platforms.

. Analyse and validate simulation results, and publish research findings in high-quality international journals and conferences.

. Contribute the development of research proposal, technical report and other research related work assigned by the Principal Investigator.

Qualifications

. PhD in Mechanical Engineering, Chemical Engineering, Fire Safety Engineering, Energy Systems, or a related discipline.

. Strong expertise in computational modelling (OpenFOAM, Chemkin or Cantera).

. Familiarity with two-phase flow modelling and GPU computing is an advantage.

. Strong programming and data analysis skills (Python, MATLAB, C++, etc.).

. Demonstrated ability to publish in high quality peer-reviewed journals.

. Strong teamwork and communication skills for collaborative research with experimental partners.

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Job ID: 153388573

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