61 machine-learning "https:" "https:" "https:" positions at Nanyang Technological University
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robotic middleware (e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic
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at the interface of optimization, geometry, and machine learning Designing, implementing, and testing algorithms Engaging in scientific exchange with collaboration partners of the project Preparing reports
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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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. Strong programming skills in Python and experience with software development for data analysis or machine learning applications. Knowledge of artificial intelligence and machine learning techniques
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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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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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areas: wireless security, wireless communications, AI/machine learning for communications, covert communications, signal processing, and/or RF design. Ability to conduct research independently and
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relationships and apply data-driven or machine learning approaches to guide molecular design and accelerate materials discovery Collaborate with internal and external stakeholders, including computational, data
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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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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