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will play a key role in building a parallelized, agent-driven exploration system and integrating a multimodal detection pipeline, ensuring real-time performance, scalability, and deployment readiness in
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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good understanding of the physics of scattering and antenna radiation Programming experience in C/C++ is necessary while experience in parallel and GPU computing is most desired More Information Location
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parallel training, and end-to-end modern machine learning pipelines. Ability to conduct independent research, critically engage with emerging challenges in AI efficiency and sustainability, and collaborate
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Professor will be responsible for teaching and research in some of these areas: computer architecture, media, database, algorithms, parallel and distributed systems, etc. Only shortlisted candidates will be
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/C++, and Python. Working knowledge for parallel programming models (MPI, OpenMP, CUDA, SYCL) Proven track record in GPU driver development, performance profiling, and debugging. Familiarity with Linux
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interrogate appropriate experimental systems, while monitoring insights arising from parallel genetic, biochemical and molecular work in disease models at A*STAR. Qualifications A PhD in cell biology
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preferably) Strong skills in turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and
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model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy