105 programming-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "UNIV" positions at KINGS COLLEGE LONDON
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geography/remote sensing, ecology, statistics, engineering, quantitative social sciences, or a related discipline. Experience in developing models and mapping with real world data, with strong programming
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, data science, quantitative social sciences, or a related discipline. Experience in developing models and mapping with real world data, with strong programming proficiency in R or Python and version
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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven
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an agreed plan of work, as well as having an ability to work independently and under own direction A passion for change in lived experience and mental health research, and an ability to champion
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be highly organised and have a demonstrable ability to plan, prioritise, and work to deadlines. The successful candidate will have an excellent attention-to-detail skill set and should be flexible
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. Ability to research, manipulate and analyse spend data for interpretation by self and others. Ability to work independently to take ownership of tasks, plan and prioritise workload, ensuring a timely
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-impact research aligned to the research themes of the Roger Williams Institute of Liver Studies (RW-ILS), while developing and maintaining an externally funded research programme. Core outputs will include
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to plan and coordinate multiple activities, including training sessions, community engagement events, audits, and reporting, ensuring all deadlines and priorities are met effectively. Facilitation and
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-oriented programming principles and Moodle’s backend structure. Comfortable working with Database Structure (e.g. MySQL) Experience using distributed version control systems such as GIT Understanding
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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven