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Field
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research in areas of concrete decarbonization, utilization of marginal and nonconventional pozzolans, prediction and mitigation of alkali-silica reaction, and development of sensors for assessing concrete
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, predictive models of neurodegenerative disease with a focus on Alzheimer's Disease. Computational models will be developed that utilize data obtained from a wide range of experiments, from basic biochemical
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, predictive assembly of complex microbial communities, and programming of self-organized pattern formation in engineered bacteria. Duke is an Equal Opportunity Employer committed to providing employment
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validation to improve understanding and prediction of subsurface processes. Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU
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. This work is to be done as a part of a BNL Laboratory Directed Research and Development (LDRD-B) project, focused on gathering experimental characterization of materials predicted to have non-relativistic
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drug and genetic screens (e.g., DepMap, PRISM) Common bioinformatics resources and pipelines (e.g., NCBI, Ensembl, BWA, STAR, GATK, VarScan, DESeq) Proficiency in Linux and programming languages such as
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental
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and optimize molten salt thermophysical property measurements, develop and utilize theoretical models and frameworks to predict salt properties, molten salt thermophysical property database expansion
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related field. The ideal candidates will have experience in one or more of the following topics: deep learning for image and point cloud data processing, deep learning for time series data prediction
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analysis, radiation outcomes prediction and adaptive radiation applications. Works closely with our faculty to understand clinical needs and implement AI solutions to address specific challenges. 2-Monte