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Field
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Qualifications* PhD Degree in Engineering, Computer Science, Data Science, Applied Mathematics, Statistics, or a related field Familiarity with (biomedical) signal processing Experience working with clinical data
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work on both probabilistic modeling, software development, and cancer biology analysis for this position. PhD in computer science, computational biology, or related quantitative field. Strong oral and
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Requirements: Preferably PhD in computer science or related field. Expertise in computer programming Knowledge in machine learning Proven research ability as evidenced through a portfolio of publications and/or
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recruiting a post-doctoral researcher to develop computational models to study the spatial organization and microenvironment interactions in tumors using spatial multiomic data. The lab focuses on mathematical
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Job no:520746 Work type:Full Time Location:Crawley Categories:Engineering and Mathematical Sciences, Social Sciences Lead numerical modelling of carbonate sand for offshore foundations, support
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Integreat develop theories, methods, models, and algorithms that integrate general and domain-specific knowledge with data. By combining the mathematical and computational cultures, and the methodologies
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to society. Required Qualifications* PhD in applied mathematics including mathematical biology, computer science, engineering, statistical physics, biology, or a related field. Reasonable communication skills
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training leading to the successful completion of a PhD degree. The fellowship requires admission to the PhD program at the Faculty of Mathematics and Natural Sciences. The application to the PhD program must
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collaborators. The post is currently available for a start date as soon as possible from 1st July 2025, but later start dates can be negotiated. You will have a PhD in Astrophysics, Applied Mathematics, or a
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decision-making and research findings. Qualifications & Competencies: Minimally a PhD degree in Artificial Intelligence, Computer Science, Optimization, or a related field. Strong foundation in multi-agent