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or ability (Python, PowerApp, Xcode, etc). •Preferred general understanding of research life cycle (equipment, testing, validation, industry sponsored research/grants, statements of work, proposal development
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Python or C++ or Matlab) and scientific computing are essential; together with MR image processing skills. Experience in multi-modal MRI processing, MRI harmonisation and statistical analysis would be
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that capture ILC-specific morphology and biology while remaining robust to differences between hospitals, scanners, staining procedures and protocols. The researcher will investigate self-supervised and
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electronic test and measurement equipment, investigation of transient electrical behaviour, comparison of different device designs and fabrication variants, and support for the evaluation of device performance
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along with experience in bioinformatic or population genomic analyses, including processing high-throughput sequencing (NGS) datasets. Strong programming skills (e.g., Linux/Bash, R, Python) and
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from largely unlabelled data that can generalize across different radar configurations and be efficiently adapted to multiple downstream perception tasks with limited labelled data. An important research
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of large-scale biological datasets will be considered a strong advantage. Experience with CRISPR-based approaches or statistical programming in R or Python is desirable but not required. The application
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ultrasound sensing and hyperspectral imaging is necessary. Proficiency in Python programming, particularly with Python libraries such as Spectral Python (spectral), NumPy, SciPy, and OpenCV and familiarity
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to it. Those three problems need three different answers, and routine NHS data can tell them apart. This is a 12-month post in the first instance, with scope to extend. It runs from a review of the
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reduces the reproducibility of scientific research, as different researchers make different decisions. A Bayesian multiverse is a systematic way of reducing these adverse effects and aims to make