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Field
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. • Evidence of strong publication record in top academic journals. • Proficiency in programming languages, particularly in Python or Matlab for automation and data analysis tasks. • Prior
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Management/RF/ADC/DAC IC design Proficiency in Cadence Proficiency in Python and Matlab Experience in implementing algorithms for machine learning Good written and oral communication skills We regret to inform you that only shortlisted candidates will be notified Hiring Institution: NTU
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(Python/R, ML/stats, reproducibility). Department Contact for Questions Applicants should submit a 1-2 page cover letter/research statement, a CV, and contact information for 3 references through
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relation to temperature-related health risks. Have experience in statistical and spatiotemporal analysis using R, Python, MATLAB, or similar analytical tools. Have good organizational and communication
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, Security, and AI Generation Models. Experience with deep learning frameworks such as PyTorch or TensorFlow. Proficiency in programming languages including C/C++, Python, Java, and Go. Familiarity with
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., Python/C/C++/Fortran). Experience with HPC (Linux, batch systems) and handling large simulation datasets (I/O, post-processing, analysis). Background in theoretical physics with at least basic knowledge
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programming languages, e.g. C/C++, Python. Experience with Machine learning technologies (e.g., LLM, and Machine Learning) is an advantage Experience with Hardware, e.g., FPGA acceleration, is an
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programming languages, e.g. C/C++, Python. Experience with Machine learning technologies (e.g., LLM, and Machine Learning) is an advantage Experience with Hardware, e.g., FPGA acceleration, is an
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Proficiency in programming languages (e.g., Python and C++), and familiarity with AI frameworks (e.g., PyTorch). Familiarity with multimodal AI frameworks, large language models (LLMs), or vision-language
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Proficiency in GUI development (e.g. using Python Dash , Tkinter , or web-based frameworks ) Experience working with data-driven simulation and model validation in power grids (Ph.D level is mandatory