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nano-mechanics, and machine learning as it applies to the field of computational mechanics. Candidates will be given opportunities to develop their teaching experience by designing and teaching a class
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Cancer Registry, as part of the national SEER registries. The postdoc fellow will work closely with statisticians, computer scientists, oncologists, and epidemiologists in the lab and other collaborating
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include, but are not limited to, using the latest computational learning-driven approaches, including computational social science, foundation models and multimodal machine learning, to enhance
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and machine learning based software to assist clinical workflow and pre-clinical studies. Recent software developed from the group has been adopted in the clinic and preclinic labs. The scientific
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. Expertise in computational neuroscience software (e.g., MATLAB, Python) as well as statistical methods and statistical packages (e.g. SAS, R). Experience with machine learning methods is preferred
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. Develop and apply ab initio computations, molecular dynamics simulations, and machine learning models. Collaborate with other researchers within the group and external partners. Present research findings
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, robust, and reproducible data analysis. Conventional statistical approaches will be combined with innovations in interpretable machine learning to address each aim from multiple angles. Analysis code will
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a unique opportunity to work in a cutting-edge, interdisciplinary environment, leveraging a novel in-vitro model of the human uterus and/or cutting edges machine learning techniques to make
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clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating
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. • Develop computational and theoretical models that bridge neural data and behaviour, leveraging modern machine‑learning toolkits. • Drive multi‑lab collaborations across SCENE; co‑author high‑impact