Algorithms for the simulation of classical dynamical systems (including climate-relevant targets such as transport, multiscale flows, and extremes)
Signal processing, time evolution, and structure-preserving transformations
We invite excellent candidates in classical machine learning to join a collaborative research initiative between the MathEXLab of NUS (Mechanical Engineering) and the Centre for Quantum Technologies (CQT). The project benefits from access to both leading experts in the field and advanced quantum computing infrastructure.
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A Ph.D. in Computer Science, Statistics, Applied Mathematics, Data Science, or related fields is highly desirable.
A strong publication record as a first author in top AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ACL) and journals (e.g., TPAMI, JMLR, AIJ), or contributions to high-impact journals such as Cell, Nature, and Science.
Proficiency in differentiable programming frameworks such as PyTorch, TensorFlow, and JAX.
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Successfully complete research tasks that yield innovative outcomes.
Develop algorithms, prototypes, theoretical frameworks, tools, analyses, insights, or datasets that address critical aspects of our overarching research objectives.
A strong commitment to academic and research excellence.
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Leading funded research projects, supervising research students, and preparing research proposals and reports.
Possess a PhD in a related discipline (transportation engineering, operations research, computer engineering/science, or related disciplines) by December 2024.
Strong knowledge and expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques.
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Leading funded research projects, supervising research students, and preparing research proposals and reports.
Possess a PhD in a related discipline (transportation engineering, operations research, computer engineering/science, or related disciplines) by December 2024.
Strong knowledge and expertise in discrete choice modelling, statistical modeling, traffic simulation, machine learning, and optimization techniques.
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Data Governance: Partner with NUS IT to keep NUSMed staff informed of data governance policies, and maintain the Data Steward/Manager registry to uphold strong governance standards.
Technology Adoption: Stay current with emerging technologies and tools in collaboration with NUS central teams, and pursue annual upskilling and training in relevant skillsets.
A Bachelor's degree in Business, Information Technology, Computer Science, Data Science, Systems Management, Statistics, or a related field.
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