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Field
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, including image acquisition, processing, analysis, and interpretation Develop and validate new imaging techniques, algorithms, or software to improve diagnostic accuracy and patient outcomes Collaborate with
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or Mathematics, Computer Science or another quantitative field, but higher qualifications (Master’s or PhD) is preferred. Excellent knowledge of at least one of the following scripting/programming languages
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(essential) / PhD (desirable) in Computer Science, AI, or a related field. Essential skills/ experience required include: Strong Understanding of machine learning algorithms, including deep and federation
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range of computing subjects, including Cyber Security, Programming, Algorithms, Computer Logic and Architecture, Software Engineering, Database Design, both at undergraduate and postgraduate level
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Khani. Job Duties: Developing new models, optimization algorithms, and machine learning algorithms for transportation systems and services (40%). Applying the models and algorithms to new transportation
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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(PSDM) techniques to reduce wiring complexity, and optimise signal demodulation algorithms for accurate, real-time battery monitoring. The position will be based in the Department of Engineering, King’s
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, applying and validating your algorithms through laboratory experiments. A PhD in Chemistry, Biology, Computer Science, or related fields with a focus on protein engineering. Proficiency in Python programming
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the future of pediatric oncology, neurodegenerative disorders, and sickle cell disease. Job Responsibilities: Analyze biomedical data with minimal supervision by performing advanced analysis, algorithm
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algorithms, models, and techniques. - Perform rigorous benchmarking and evaluation of the developed models. - Optimize algorithms for efficiency and scalability. Collaboration & Mentorship - Work closely with