134 python "Computer Vision Center" "Computer Vision Center" Fellowship positions in Singapore
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background with hands-on Python skills. Experience with predictive or uncertainty-aware modelling, AI-guided experimentation or related data-driven methods. Experience with materials, manufacturing
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machine learning/data science, with hands-on Python and experience with modern ML frameworks. Experience with one or more of time-series/sequence modelling, state estimation, constrained or Bayesian
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field by the appointment start date. Strong econometric theory, probability, statistics, and research skills in IV methods, causal inference, and machine learning. Proficiency in R, Python, MATLAB
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generation, and hallucination mitigation. Strong Python skills and experience with PyTorch, modern LLM frameworks, APIs, microservices, and evaluation of retrieval precision/recall and factuality. Experience
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• Excellent coding skills in python with pytorch (distributed deep reinforcement learning, Transformers, etc.) • Literature review/summarizing skills
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relevant datasets using Python, MATLAB, SQL, and related analytical tools. Formulate research hypotheses, design empirical studies and experiments, and conduct robustness and validation analyses. Develop
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learning for materials discovery or quantitative image analysis, DFT calculations for catalyst design, experiences with MATLAB / Python / AutoCAD / COMSOL • Organic synthesis, polymer chemistry, synthesis
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machine learning methods • Proficiency in transport planning software such as EMME or PTV Visum. • Programming expertise in at least one language, e.g., Python, Java, or C++. • Strong written and
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manuscripts for international peer-reviewed journals and collaborate effectively with international research partners. Programing and data analysis skills (e.g., MATLAB, Python, R). Prior experience in in-vivo
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-scale firm-level or related datasets. Proficiency in Python and/or Stata for data management, statistical analysis, and empirical research. Experience in merging and managing large datasets from multiple