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work directly with the program lead, Prof Rose McCab, Dr Alexandra Bakou (Trial Manager) and Maria Long (Trial Manager) in the School for Health Sciences, City, University of London.. The post will be
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team and contribute to a programme of work led by Prof Pratik Choudhary around type 1 diabetes and technology. The appointed individual will have the opportunity to contribute to the real-world evidence
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directly with the program lead, Prof Rose McCab, Dr Alexandra Bakou (Trial Manager) and Maria Long (Trial Manager) in the School for Health Sciences, City, University of London.. The post will be based
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provide support to the PI (Asst. Prof. Kean J. HSU) and other project team members on overall research administration for the projects, including coordination of research staff and trainees based on a lab
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learning algorithm to analyze how experimental parameters ( 3D printing and electrospinning) affect the design. At least 3 semesters of FIU work experience or related experience outside the University with
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candidate may be employed at Grade E or Grade F depending on qualifications. The project is led by Prof. David Crundall. You will work with a small team to help create and validate our training course, and
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: £38,482-£43,259 per annum, including London Weighting Allowance Job ID: 118787 Close Date: 22-Jul-2025 Contact Person: Prof Catherine Evans Contact Details: Catherine.evans@kcl.ac.uk
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for motion control, and monitoring sensors. Job Requirements BS in Electrical and Electronic Engineering, Mechanical Engineering, computer engineering, or related field 4-year research experience in related
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engine. Algorithm Development: Contribute to designing, prototyping, and testing algorithmic models for personalized content recommendations. System Evaluation: Support the evaluation of the recommendation
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques