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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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undergraduate degree in a relevant computational domain, such as engineering, physics, neuroscience, computer science, applied mathematics. Skills in computer programming (especially Python or C++ or Matlab) and
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such as, programming languages (i.e. Python), knowledge of the handling of digital imaging and communications in medicine (DICOM) and some experience with picture archiving and communication systems (PACS
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proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
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/computational biology/molecular biology/genomics or related area. The successful candidate will have considerable experience in computational bioinformatic analysis (R, Python or equivalent) of omics data and
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development), and strong programming skills (Python preferred). As a people-orientated individual, you will have the ability to influence and engage collaboratively with AEGIS, UoN and other stakeholders. As a
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with coding, ideally in Python or MATLAB Funding support This studentship is open to Home students only. It is jointly supported by the Faculty of Engineering and industrial partners which is expected
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including next-generation sequencing, bioinformatic analysis (R and/or python), mammalian cell culture. The post holder will be comfortable working both independently and in a collaborative environment. What
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, alignment, and characterisation of laser-based instrumentation. • Strong experience with computer programming, both for signal processing and experimental hardware control (e.g., C, C++, Python, MATLAB