Methods in machine learning for probabilistic modelling of environment, with applications in meteorology and geology

天气预报的重要性怎么强调都不为过：它对一系列决策都有影响，从打伞到是否应该取消航班或疏散地区。它形成了一个重要的研究领域，汇集了数学、统计学、数据科学和物理等学科。准确和可靠的天气预报是有价值的，但在实践中，它们依赖于不完善的信息。基于物理的数学预测模型为了预报天气而进行近似，而过去和现在的天气数据不能被准确测量，如果真的有的话。此外，有许多相互竞争的预测模型(系统)使这项任务变得更加具有挑战性。这一博士生将专注于研究和开发统计和数据科学技术，以最佳地融合所有可用于预报天气的信息源，同时量化所有不确定和误差的来源。该项目将着眼于一系列方法，以实现以最佳方式合并各种数据源的目标。这些技术包括(但不限于)统计建模技术，例如分层贝叶斯建模、贝叶斯融合和机器学习算法。对业务天气预报的另一个挑战是，任何这样的技术都需要实用和计算效率。因此，大部分工作将集中在如何在计算机上以最佳方式实施已开发的技术。重点还将放在定义适当的指标上，以便将开发的技术彼此进行比较，以及与当前的“基线”方法进行比较。这个项目为学生提供了一个独特的机会，让他们在数据科学、统计学和机器学习的高级可转移技能方面获得经验。与此同时，这项工作的动机是提高天气预报准确性的现实世界挑战，这提供了在英国气象局(UKMO)工作和与其合作的机会。这名学生将驻扎在埃克塞特斯特里瑟姆大学的校园里，但也将在埃克塞特的英国气象组织与那里的科学家合作。英国气象组织将提供个案支持，使学生受益于英国气象组织天气预报小组的专业知识和天气预报方面的培训。学生还将通过参加英国、欧洲以及国际上的相关研讨会和会议来向研究社区传播他们的工作。这项工作有很强的最终用户因素，特别是可再生能源行业，因此学生还将与国家电网等强烈支持该项目的天气预报用户共度时光。这一资助的博士奖学金的理想候选人应该具有量化背景，并对数据分析和统计、数据科学和机器学习等领域感兴趣。他们还应该对计算感兴趣，因为据设想，开发的技术将在最先进的云计算上实现。该奖学金包括英国的学费以及每年14,553 GB的维修津贴。它还包括培训课程和旅行(例如参加会议)等发展费用。

The importance of weather forecasting cannot be overstated: it has impacts on a range of decision making, from taking an umbrella to whether flights should be cancelled or areas evacuated. It forms an important area of research which brings together disciplines such as mathematics, statistics, data science and physics. Accurate and reliable weather forecasts are valuable, but in practice they rely on imperfect information. Mathematical forecasting models based on physics make approximations in order to forecast weather, while past and current weather data cannot be measured accurately, if at all. In addition, there many competing forecasting models (systems) making the task even more challenging. This PhD studentship will focus on investigating and developing statistical and data science techniques to optimally blend all sources of information available for forecasting weather, while at the same time quantifying all sources of uncertainty and error. The project will look at a range of methods for achieving the goal of optimally merging various data sources. These include (but are not limited to) statistical modelling techniques such as hierarchical Bayesian modelling, Bayesian melding and machine learning algorithms. An added challenge to operational weather forecasting is the need for any such technique to be practical and computationally efficient. Therefore much of the effort will be on the optimal implementation of the developed techniques on the computer. Emphasis will also be on defining suitable metrics with which to compare the developed techniques with each other as well are with current "baseline" approaches. This project offers a unique opportunity for a student to gain experience in advanced transferable skills across data science, statistics and machine learning. At the same time, the work is motivated by the real world challenge of improving weather forecast accuracy, which provides the opportunity to gain experience in working for and with the UK Met Office (UKMO). The student will be based at the University of Exeter Streatham campus, but will also be expected to spend time at the UKMO in Exeter, collaborating with scientists there. The UKMO will provide CASE support so that the student will benefit from expertise in the weather forecasting team at the UKMO and training with respect to weather forecasting. The student will also be expected to disseminate their work to the research community by attending relevant workshops and conferences across the UK, Europe but also internationally. There is a strong end-user element motivating this work, specifically the renewable energy industry, so the student will also be spending time with weather forecast users such as National Grid who strongly support this project. The ideal candidate for this funded PhD scholarship should have a quantitative background and be interested in data analysis and fields such as statistics, data science and machine learning. They should also have an interest in computing, as it is envisioned that the techniques developed will be implemented on state-of-the-art cloud computing. The scholarship includes UK tuition fees as well as £14,553 maintenance allowance per year. It also includes cover for development such as training courses and for travel (e.g. attending conferences).