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machine learning, computer vision, human-computer interaction, or similar relevant areas. Experience in research or development on bias, interpretability, and/or privacy in machine learning/AI is necessary
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candidate will help to support the team to formally document algorithms within a quality management system. EpiNav™ provides state-of-the-art computer-assisted support for the planning of stereotactic
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becomes essential. This project will focus on building a comprehensive digital twin of a future quantum computer to investigate how classical subsystems scale and interact, and how this scaling impacts
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the risks. You will have: a PhD in one of the relevant STEM disciplines, such as mathematics, statistics, computer sciences, theoretical food, ecological or physical sciences, etc. skills in mathematical
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computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying statistical and machine learning to real life problems, using a
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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and statistical modelling, statistical image analysis and computer vision, chemometrics, biophysics, bioengineering. Preference will be given to candidates with a demonstrated experience in applying
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or military collaborators. You are expected to have strong mathematical and programming skills, knowledge of computer vision and data mining tools, and be able to work on group software projects using modern
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patients with hearing disorders. You will join a collaborative environment where your technical expertise in computer vision will contribute to clinical applications. Our team are now expanding our work