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), arrest and/or conviction record, military or veteran status, sexual orientation, gender expression and/or identity, an individual’s genetic information, domestic violence victim status, familial status
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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approaches, machine learning) where appropriate. The successful candidate will actively promote FAIR data practices and will have opportunities to contribute to teaching, training, and wider community
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the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning
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. This group uses state-of-the-art Earth observation data and advanced computer techniques to study the Polar regions. We specialise in using Synthetic Aperture Radar (SAR) and altimetry satellite data
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the industry to lead and/or conduct innovative research on, but not limited to evolutionary computing, job scheduling, transfer optimization, transfer learning, reinforcement learning, large-scale
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ethical and security standards. Concept and Algorithm Development: Innovate in data science, machine learning, and AI. Data Analysis and Reporting: Contribute to data analysis, reporting, and publication
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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Requirements: A PhD degree in mathematics or related areas, with a strong background in topological data analysis (TDA) and machine learning on biomolecular data Proficiency in programming languages such as
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at the Institute of Genetics and Cancer. Informal enquiries may be directed to Dr Athina Spiliopoulou (A.Spiliopoulou@ed.ac.uk ). Your skills and attributes for success: PhD in machine learning, genetic epidemiology