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Field
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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AI to predict safety outcomes for multiple targets and combination therapies Collaborate with research teams and data scientists to design data-driven strategies using machine learning/AI methods
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at Northeastern. (10%) Required Qualifications: - Ph.D. in Human-Computer Interaction, Information Science, Computer Science, Design, or related fields - Strong record of published research in HCI, CSCW, DIS
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consequences to sensitive species. Revisions also include updating values in the existing models that reflect new science and lessons learned over the last 15-years. The toolkit also includes evaluation tools
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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receive guidance from a mentor and will learn about conducting research analysis within OP. You will engage with current OP staff on policy analysis projects and assignments in one of the following focus
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evaluate machine learning approaches for predicting clinically successful drug targets. For this work, the postdoc will have access to a large high-performance compute cluster and to AbbVie's cutting-edge