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Field
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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. To set up and analyze experiments and datasets under different conditions. To propose a methodology/framework in a software prototype to be developed in the project. To report research findings in a form of
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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skills in image processing or genomic data analysis. Proficiency in rodent neurosurgeries. Familiar with Linux OS, virtual environment working and batch scripting. Programming proficiency in R, Python
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involving speech/audio, images, and video along with text-based applications. Job Requirements: MSc (Research Associate) or PhD (Research Fellow) in Electrical Engineering, Computer Science, Statistics
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synaptic and cellular studies using patch-clamp electrophysiology, large-scale population recordings using 2-photon Ca2+ imaging in awake behaving mice, and advanced modeling + machine learning analyses
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plasticity. We study questions at a variety of levels, Ranging from synaptic and cellular studies using patch-clamp electrophysiology, large-scale population recordings using 2-photon Ca2+ imaging in awake
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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services, as well as receive clinical training in inherited cardiac conditions, sports cardiology and cardiac imaging. Person Specification Applicants should be clinically qualified with ALS and MRCP
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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