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Field
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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large AI models or their applications. Current PLI projects involve work with language, images, multimodal data, science/math, code, and scientific data. PLI is also focused on AI safety, fairness, and
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biology with closely allied clinical programs in Medicine and Surgery to improve the diagnosis and treatment of cardiovascular disorders. Qualifications and Responsibilities Experience in immunology
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in stem cells, tissue engineering, microfluidics, organ-on-a-chip, 3D bioprinting, and non-invasive imaging to mimic various microphysiological features of tissues in health and diseased states and for
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etching. Use ‘zoom’ tomography and imaging to resolve structural variation across scales from 30μm down to 3nm to establish a platform for reverse bottom-up enamel remineralisation. Bottom-up multi-modal 4D
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modifiers. The cultures will be analysed using basic molecular and cell biological techniqu es as well as high throughput imaging and analysis to observe modifier effects on LRRK2 and LRRK2 Rab substrate
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techniques include motor/behavioral tests of mice, in vivo compound administration, postmortem tissue harvesting and processing with a focus on the nervous system, cell tracing, immunohistochemistry, mouse
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strategies that modulate the crosstalk between tumor and immune cells, and develop new image-based screening technologies to identify targets that mediate clinically-relevant cell-cell interactions
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hands-on experience with advanced techniques, including patch clamp, optogenetics, fluorescence imaging, auditory brainstem response (ABR), stereotaxic surgery, RNA-seq, and computational modeling