419 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in Singapore
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this project for external grant Job Requirements: A PhD degree in material sciences or applied physics, with focus on renewable energy Evidenced experience in DFT and machine learning especially its application
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Investigator (PI) or team lead with project management tasks. Job Requirements: PhD degree in Optimization, Artificial Intelligence, Transportation or Aerospace. Evidence of developing Machine Learning and
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to Quantum computation, including the entangled photon source, electro-optics modulator fabrication, number resolving detectors. Job Requirements: PhD degree in either engineering, applied physics or relevant
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found online: https://www.jglab.org/ and www.github.com/goekelab Requirements: PhD in Bioinformatics, Computer Science, Statistics, Genomics, or a related discipline Good programming skills and strong
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The Centre for Quantum Technologies @NTU (CQT@NTU) is seeking to hire a Research Fellow. Key Responsibilities: Quantum computation research in CQT. Generate multi qubit entangled state and explore
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The Centre for Quantum Technologies @NTU (CQT@NTU) is seeking to hire a Research Fellow. Key Responsibilities: Quantum computation research in CQT. Generate multi qubit entangled state and explore
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colleagues/collaborators who are engineering the microbes. Qualifications Possess a PHD in systems biology, computational biology, or other related fields with relevant research experience. Have computational
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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to apply advanced AI models in areas such as catalyst design, multi-scale modeling, and spectroscopic analysis. The Research Fellow will take on a significant role in machine learning theoretical energy
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: PhD in Materials Science, Chemistry, Physics, Computer Science, or a related field. Strong expertise in machine learning for materials science (e.g., generative models, neural networks, active learning