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. Proficiency in Python and/or machine learning applications for data analysis. Ability to work independently and manage multiple research activities. Experience contributing to academic publications and
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primarily involves leading independent neuroimaging analyses and developing innovative analytical approaches to address the scientific aims of the lab. Multiple datasets are already available in the lab
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and across individuals, populations, and species. To understand this variation, we need to consider how stress responses adapt to the structure of the environment over multiple timescales—through
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the reproducibility and robustness of quantitative MRI maps across multiple clinical MRI platforms (1.5 T and 3.0 T – Siemens, GE, Philips) using test objects (phantoms). - Coordinate MRI acquisitions on a cohort of
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and heterointerfaces. The postdoc will lead experimental design, data acquisition, and quantitative reconstruction. The appointees will work within a highly collaborative team spanning multiple DOE user
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stress responses within and across individuals, populations, and species. To understand this variation, we need to consider how stress responses adapt to the structure of the environment over multiple
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. Basic Qualifications: A PhD degree in civil, chemical, or environmental engineering. A minimum of 2 years of experience in the use of Python for programming of data analytical models and algorithms
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the use of programming languages such as R and python; Experience in analysing large and complicated datasets, preferably including geospatial data; Experience in, or strong affinity with, mechanistic and
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candidate will lead computational lab analysis work across multiple projects, largely focussing on the perturbation of skeletal muscle cells (myofibres) during periods of environmental and/or genetic
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they link to C cycling process; Excellent computational, analytical and statistical skills, including proficiency in the use of programming languages such as R and python; Experience in analysing large and