98 machine-learning-phd "The Art Institutes" positions at King's College London in United Kingdom
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the introduction to your advert, where you can provide a small summary of King’s, your faculty / department this role is located. King’s vision is to be the UK leader in online and blended learning by
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independently without direct supervision 8. Proven experience in analysing high-throughput genomic data, particularly WGS and GWAS datasets. Desirable criteria 1. UpToDate knowledge of machine learning
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develop a software to automate data processing, machine learning model selection and optimisation and enable end-users to design and optimise microbial protein solutions with minimal user intervention
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optimized for inference with the architecture and collaborate with project partners involved in the experimental demonstrations of various Machine Learning use cases in the new hardware. You will also have
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. UpToDate knowledge of machine learning methods applied to clinical or omics data 2. Research experience in Neuroscience 3. Experience in grant writing 4. Experience in supervising staff and
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-quality research publications in peer-reviewed journals and international conferences 4. Knowledge of Python and Python-based computer programming Desirable criteria 1. A PhD thesis relevant to
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editing, live cell imaging and plasmid design/cloning. Those willing to learn or develop new technologies are particularly welcome. This is a full time post (35 Hours per week), and you will be offered a
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compliance with high standards, and to actively supervise the less experienced. An ability to interact and learn is crucial. Candidates should hold a PhD, or have successfully submitted, a PhD thesis, working
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backgrounds and experience. Candidates with a PhD and relevant research experience will be considered for Research Associate. This post will be offered on a fixed-term contract to 31 March 2027. This is a full
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post. Key Responsibilities Proactively contribute to undergraduate and postgraduate education and supervision, including convening and examining modules. Inspire and foster students’ learning and