1,113 machine-learning-"https:"-"https:"-"https:"-"Babes-Bolyai-University" Fellowship positions
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modelling, or machine learning). Proficiency in statistical programming (e.g., R, Python, Stan, or similar). Experience developing and managing longitudinal studies. Evidence of contributions towards
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | about 3 hours ago
such as CAR-T cells and develop novel strategies to improve safety and efficacy of T cell therapies. Learning Objectives: The overall learning experiences you will gain are as follows: Will learn to perform
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to cell and gene therapy. Will learn to use advanced manufacturing tools and strategies to gain a deeper understanding of challenges associated with T cell-based immunotherapies (such as CAR-T cells). Will
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas
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causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design, implementation
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combining causal machine learning, transport behaviour analysis, and residential energy demand modelling to support sustainable urban and energy policy. The researcher will contribute to the design
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. Develop skills in coupling crop and hydrology models at watershed scales. Gain experience validating models using large, multi-source datasets. Learn to apply high-performance computing and machine learning