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Field
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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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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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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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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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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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. The employment period will be three years of full time with no teaching obligations, and there is a premise for employment that the PhD Research Fellow is enrolled in USN’s PhD program in technology within three
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oncology grouping sees close to 550 new cases of these cancers a year. There is a large active clinical trials programme that spans phase 1 to phase 3. These patients have a poor prognosis, and this clinic
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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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travel to Kenya for fieldwork. About You You will have a PhD in malacology, epidemiology, spatial mapping, mathematical modelling or related disciplines and be able to demonstrate a sound understanding of
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computer science or statistics A solid background in mathematics, linear algebra and statistics. Documented experience with Bayesian spatiotemporal modelling, including experience with the INLA framework