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Field
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-making. This may include optimising transport/manufacturing operations, alternative fuel/ technology pathways, infrastructure capacity, fleet/machine composition, resource allocation, scheduling
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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
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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
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. 5. Familiarity with scientific machine learning and data-driven modelling.
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) years in Computer Science, Data Science, Computational Engineering, Bioinformatics, Computational Social Science, or another highly quantitative field with substantial emphasis on machine learning, deep
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. Knowledge of data-driven analytics, machine learning, signal processing, or advanced modelling techniques relevant to power systems. Experience with real-time simulation platforms, hardware-in-the-loop
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-science and signal-analysis tasks, including processing experimental signals, integrating datasets, developing machine-learning models, and mapping measured fuel properties to SAF performance. For the post
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experience in, for example, machine learning, high-performance computing, reconfigurable hardware, cloud computing, compiler engineering, distributed systems, or DevOps are encouraged to apply, as
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or thermal properties Expertise in AI and machine learning is advantageous Excellent written and verbal communication skills Ability to work independently and as part of a team We regret to inform that only
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, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model-predictive control, etc