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Requisition Id 15358 Overview: Oak Ridge National Laboratory (ORNL) is seeking an ambitious postdoctoral scientist with keen interest in artificial intelligence (AI) / machine learning (ML) and
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analyze and interpret multi-omic data to identify spatial patterns, cellular neighborhoods, and gene programs associated with drug resistance. Develop predictive models to infer tumor evolution and
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, scikit-learn, TensorFlow, PyTorch). Hands-on experience with data science workflows, including ML/AI model development, training, and evaluation for predictive analytics or decision support. Excellent oral
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technologies—and eager to accelerate their discovery with machine learning and materials theory? Are you passionate about linking atomistic processes to device performance through computer simulations? Are you
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transformer-based architectures to create a powerful tool for understanding and predicting bacterial genomic sequences. The successful candidate will play a key role in developing and optimizing these models
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expertise in the area of prediction of both costs and benefits of retraining or finetuning machine learning models in application domains including financial fraud detection and machine translation
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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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have: Expertise in processing optical remote sensing data over large areas and using machine learning models for predicting ecological metrics Strong background in remote sensing in hyperspectral imaging
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-scale epidemiological datasets using statistical and machine learning methods Conduct systematic literature reviews and meta-analyses on disease dynamics topics Develop and validate predictive models
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Post Doctoral Researcher in Human-centred Large Language Models for Software Engineering, Departm...
to Software Engineering for some time, and in areas such as software effort estimation, defect prediction, and project management in general. And more recently, the proposal of Large Language Models opened a