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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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(R2) Application Deadline 4 May 2026 - 21:59 (UTC) Country Singapore Type of Contract Temporary Job Status Not Applicable Is the job funded through the EU Research Framework Programme? Not funded by a
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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Key Responsibilities: Develop and implement high-fidelity CFD and FEA simulation workflows for modelling heat
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Job Description Job Alerts Link Apply now Research Fellow in Quantum Networks University-Level Unit: Centre for Quantum Technologies Faculty/Department-Level Unit: Centre for Quantum Technologies
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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focuses on a host of Interdisciplinary Research Programs, Flagship Programs, Consortium Platform and an Accelerator Programme that covers the energy value chain from generation to innovative end-use
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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). The ideal candidate brings a strong machine learning foundation, curiosity about sound and music computing, and enthusiasm for collaborating with PhD students and postdocs. You will help combine individual
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Requirements: • PhD in Mechanical/Energy Engineering or related field with experience in building energy or thermo-fluid network modelling (Candidates who do not possess a doctoral degree but have very high