28 machine-learning "https:" "https:" Fellowship positions at Nanyang Technological University
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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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support the development of AI-driven and data-driven approaches for the discovery and design of functional materials. The role will involve the development and application of machine learning models, high
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related field). Decent programming skills, especially in Python or JAX. Familiarity with finance theory (asset pricing, derivative pricing, risk management etc.). Familiarity with machine learning or deep
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one of the following areas: wireless localization, wireless sensing, AI/machine learning for communications, or signal processing. Ability to conduct experimental research independently and
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with vessel protected volume estimates to identify safety-buffer overlaps, developing multi-agent path-planning, and machine-learning methods for corridor allocation, airspace capacity optimisation, and
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. Interface with machine learning group on database set up CO2 removal process Job Requirements: PhD in Chemistry/Materials Science/Physics Candidates with strong background in Materials chemistry/Physical
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) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5, Franka Emika
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at NTU are looking for a Research Fellow (RF) to carry out research in probabilistic machine learning and GenAI, by exploring cutting-edge approaches such as sequence model design, continual learning
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Osmosis (NF/RO) Membrane for Industrial Separation and Purification Application” Key Responsibilities: Integrating Machine Learning (ML) with Molecular Dynamics (MD) to predict phase-separation dynamics and
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by ARPES, pursue scalable wafer-scale moiré epitaxy, develop epitaxial superconductors for quantum computing and integrate machine learning for automated high-throughput MBE. We are particularly