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)—topics including, but not limited to: · Physics-informed neural networks (PINN) & neural operators · Physics-aware convolutional neural networks (PARC) · Meta-learning/transfer
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day of employment per University Policy. This position will not sponsor for work visas. Why Join Us? This postdoctoral position offers a collaborative and dynamic research environment at one
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seeking hands-on experience in biostatistical research within a dynamic academic environment and aiming to make impactful contributions to the field. For additional information about the position, please
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the intervention may not be implemented as intended Feedback and Improvement: Provide feedback to facilitators based on fidelity data Suggest improvements to enhance fidelity and address any deviations from
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Qualifications: Education: Ph.D. or equivalent degree in molecular biology, cellular biology, or a related field. Preferred Qualifications: While familiarity with the following techniques is desirable, not all
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for three professional references Applications that do not contain all required documents will not receive full consideration. Questions relating to the application process should be directed to Jeremy
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documents into the CV/Resume box. Applications that do not contain all of the required documents will not receive full consideration.*** For questions about the application process, please contact Rhiannon
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documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration. For questions about the application process, please contact Yelena
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documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration
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cardiovascular treatments. In this role you will apply both machine learning predictive modelling and human genetic analyses of non-coding regulatory sequences to identify cell-specific targets for coronary artery