125 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at King's College London in United Kingdom
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About us: At King’s College London, our Engineering Team plays a critical role in ensuring our facilities are safe, efficient and well maintained to ensure our vibrant community is able to learn
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of genomic predictors into multivariable and machine-learning prediction models for treatment outcome. Working closely with Professors Breen, Eley and collaborators across psychiatric genetics, clinical
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demonstrable ability to learn relevant technologies 5. Logical and methodical approach to problem solving and technical troubleshooting 6. Strong customer service ethos (able to cater for technical
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as trainers and observers in the GMP cleanrooms. The post holder will learn a wide range of molecular and cell biology skills such as cell culture, transfections, QPCR, flow cytometry etc. They will
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. The post is offered at a competitive salary (Grade 6, Spine Point 33 on the KCL salary scale), and includes provisions for travel money, computer equipment and academic and leadership training. This is a
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, and human-centred computing. You will join the Vision & Human-Robot Interaction (VHR) Lab, a multidisciplinary research group working at the intersection of robotics, machine learning, and human
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of Psychological Medicine focuses on the interface between psychiatry and medicine, psychiatry and occupation, psychiatry and the military, and psychiatry in different settings. The disorders of interest are those
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computer literacy (ability to access email and online training) 3. Experience of working in a catering environment 4. Understanding of all relevant health, hygiene, and safety regulations 5
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. The post is offered at a competitive salary (Grade 6, Spine Point 33 on the KCL salary scale), and includes provisions for travel money, computer equipment and academic and leadership training. This is a
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are caused and how people respond to treatments. We will investigate blood and cerebrospinal fluid from patients and use machine-learning to identify the treatment-relevant immune processes, with an emphasis