Research Fellow (Cryptography and/or machine learning interpretability)
Nanyang Technological University
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The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.
In order to complete the NRF Investigatorship research project “Interpretable and Automated Cryptanalysis for Tailor-Made Cryptographic Design”, the school is looking to recruit one research fellow on cryptography and/or machine learning.
Key Responsibilities
- The candidate will conduct research on cryptography and/or machine learning
- The candidate will help preparing reports and presentations for the project
- The candidate will publish relevant results to cryptography and/or machine learning conferences/journals
Job Requirement
- PhD in Mathematics and/or Electronics and/or Computer Science
- Ability to work independently and as part of a team with strong initiatives.
- Good communication and interpersonal skills.
- Expertise in symmetric cryptanalysis/design or in state-of-the-art explainable AI solutions is a strong plus
We regret that only shortlisted candidates will be notified.
Hiring Institution: NTUSummary
Research on cryptography and machine learning; publish results; support project reports.
Job title
Research Fellow (Cryptography and/or machine learning interpretability)
Experience level
doctoral
Industry
education
Location requirements
Singapore, on-site work only, at NTU Main Campus
Salary
Not specified
Management role
No
Required skills
Preferred skills
Specializations
Structured locations inferred from the posting.
Unknown location