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tunnel experiments is a plus • Experience in programming and AI/machine learning is a plus More Information Location: Kent Ridge Campus Organization: College of Design and Engineering Department
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topics ranging across programming language (especially Bayesian statistical probabilistic programming), statistical machine learning, generative AI, and AI Safety. Key Responsibilities: Manage own academic
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing
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a related field. Strong programming skills (R, Python) and experience with NGS data analysis. Proficiency in statistical and machine learning methods for biological data. Familiarity with high
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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Requirements A PhD degree in computer or related programmes and have relevant competence in artificial intelligence (AI). Knowledge and experience in NeRF/Gaussian Splatting will be advantageous. Able to build
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one of the following: Econometric methods for causal inference; Data science and machine learning; Survey design and analysis; Qualitative analysis skills specialized in policy and geopolitics A good
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informatics approaches (e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication
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(e.g., machine learning, Bayesian statistics) and spatial data processing and analysis skills would be of advantage. Expertise in Stata, R, or other analytic tools. Strong communication (oral and written
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to the work described herein. Qualifications Job Requirements: • PhD in Biostatistics, Bioinformatics, Computational Biology, or other related fields. • Strong foundation in statistical modeling, machine