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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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or PhD in meteorology, atmospheric sciences, climate science or related field preferred. Experience in implementing machine learning methods in weather/climate research, analysis of dynamical model (e.g
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dynamic functions of microbial communities. For this position, the major goal is to develop advanced computational modeling frameworks that combine physics and machine learning to predict, understand and
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information extraction. Relevant publications as an author in top-tier AI/NLP/ML conferences (e.g., ACL, EMNLP, AAAI). Proficient in programming languages and frameworks relevant to machine learning
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developing and deploying open-source tools that shape how malaria is tracked and tackled across sub-Saharan Africa. You will design, implement and evaluate statistical and machine learning models
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/machine learning to expand the interdisciplinary faculty cluster on Translational Predictive Biology (CTPB). The selected faculty is expected to synergize with existing Translational Predictive Biology
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of the extraction and beneficiation system. This work will require an understanding of mining processes, mathematical modeling of flows and extraction decisions, and the use of machine learning algorithms to predict
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labelled datasets for training and evaluating deep learning models Work under supervision to assist with development of machine learning models Inspect and provide feedback on model predictions This project
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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background in machine learning, predictive modeling, or applied AI Proficiency in Python and/or R; experience with libraries like scikit-learn, XGBoost, TensorFlow. -Experience working with real-world datasets