牛津大学高等研究院（苏州）招募数学建模与数据分析研究员，从事基于粗糙路径分析的机器学习技术、求解偏微分方程和随机偏微分方程的数值方法、金融模型及金融数据分析等领域的科研工作。
Oxford Suzhou Centre for Advanced Research is looking to recruite a Research Scientist in Mathematical Modelling and Data Analytics, to undertake research in techniques of machine learning based on rough path analysis, numerical methods for solving PDEs and stochastic PDEs, financial modelling and financial data analysis.
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岗位信息 The Role
招聘岗位：
The vacancy：
数学建模与数据分析研究员
Research Scientist in Mathematical Modelling and Data Analytics
任职者将成为OSCAR数学建模和数据分析中心的核心成员，将与研究中心一起根据PI在过去几十年中发展的成功理论，为自然科学和金融业中出现的问题提供解决方案，特别是关于（但不限于）序列数据的粗糙路径和signature算法。 任职者将帮助PI广泛基于随机分析、量化金融和其他相关研究领域的研究项目，最初重点（但不限于）关注于粗糙路径分析、基于signature算法的深度学习、金融建模、基于金融数据的深度学习、随机控制、随机分析。任职者将参与研究中心的日常运作，启动新的研究计划，与同事保持和发展研究伙伴关系，参与和保持与外部学术和工业合作伙伴的合作赞助研究，充当牛津和苏州平行研究工作之间的桥梁，并将与牛津-苏州高级研究中心内的合作伙伴互动。
应聘者需具备相关领域博士学位(或即将毕业)，并具有以下一个或多个领域的经验：深度学习、随机分析、数学金融、统计力学、随机控制、应用概率、统计学、数学物理学。具备随机分析的知识者优先。
职责描述：
1. 与OSCAR的PI合作研究基于粗糙路径分析的机器学习和金融建模以解决金融数据分析和大型随机系统研究中出现的问题。
2. 在随机分析、粗略路径分析、基于signature的机器学习技术、随机控制（经典或粗略路径）、金融数据模拟和分析中提出研究问题。
3. 通过使用先进的数学工具，如风险度量、随机控制、蒙特卡罗方法和随机数值模拟，对粗略路径分析、金融模型和风险管理进行理论研究。
4. 制定并采用适当的分析方案和技术来支持研究。
5. 合作撰写科学报告和期刊文章。在国际会议上发表论文，并主持研讨会，传播研究成果。
6. 在适当的情况下，协助监督研究助理和访客。
7. 为研究所的日常运作做出贡献。
8. 与合作机构和研究小组的同事开展合作项目。
The post holder will be the core team of the OSCAR Institute for Mathematical Modelling and Data Analytics. The post holder will work with the institute to establish a world-class research program in the fundamental research on stochastic analysis and quantitative finance aiming to provide solutions of problems arising from natural science and from financial industry, based on the successful theories developed in the past decades by the PIs, in particular on (but not limited to) rough paths and signatures of sequential data. In collaboration with his/her academic advisor(s) and PIs at OSCAR, the post holders will help to lead research programs broadly based in stochastic analysis, quantitative finance and other related research areas, with initial emphasis (but not limited to) on rough path analysis, deep learning via signatures, financial modelling, financial data deep learning, stochastic control, stochastic analysis and etc. The post holder will participate in the day-to-day running of research activities, launching new research initiatives, maintaining and growing research partnerships with colleagues, engaging and maintaining in collaborative sponsored research with external academic and industrial partners, serve as a bridge between parallel research efforts based in Oxford and Suzhou, and will interact with partners within the Oxford-Suzhou Centre for Advanced Research.
The successful candidate will possess a doctorate (or be near completion) in a relevant field with experience in one or more of the following areas: deep learning, stochastic analysis, mathematical finance, statistical mechanics, stochastic control, applied probability, statistics, mathematical physics. Knowledge of stochastic analysis is desirable.
Responsibilities:
1. Work in collaboration with the PIs at OSCAR to address problems arising from financial data analysis and the research for large random systems, with specific emphasis on machine learning based on rough path analysis and financial modelling.
2. Develop research questions within stochastic analysis, rough path analysis, machine learning techniques based on signatures, stochastic control – classical or via rough paths, financial data simulation and analysis.
3. Conduct theoretical study of rough path analysis, financial models, and risk management by using advanced mathematical tools such as risk measures, stochastic controls, and Monte-Carlo methods, and stochastic numerical simulations.
4. Develop and pursue appropriate analytical protocols and techniques to support research.
5. Collaborate in the preparation of scientific reports and journal articles. Present papers at national conferences, and lead seminars to disseminate research findings.
6. Assist with supervision of research assistants and visitors, where appropriate.
7. Contribute to the day-to-day running of the Institute.
8. Carry out collaborative projects with colleagues in partner institutions and research groups.
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选拔标准 Selection criteria
基本条件：
1. 拥有相关的博士/博士学位（或即将完成）；
2. 分析工作能力强；
3. 良好的口头和书面沟通及表达能力；
4. 拥有足够的学科专业知识，以制定研究项目和方法。；
5. 迄今为止取得研究成果的有力证据，如良好的出版记录和/或学术成就和会议奖项所示；
6. 具有以下一个或多个领域的经验：深度学习、随机分析、金融数学、统计学、随机控制、统计力学、数学物理、数据分析或相关科目。
分析工作能力强。
优先考虑:
1. 具有较强的代码能力，熟悉深度学习算法；
2. 具有用深度学习算法处理金融数据工作的经验；
3. 具备在数学和量化金融背景的多学科研究团队中工作的能力；
4. 在编写涉及金融工具的程序方面具有高度的能力；
Essential:
1. Hold a relevant Ph.D/D.Phil (or be near completion).;
2. High degree of competence in analytical work;
3. Good oral and written communication and presentation skills;
4. Possess sufficient specialist knowledge in the discipline to develop research projects and methodologies.;
5. Strong evidence of research achievement to date, as might be demonstrated by a good publication record and/or academic distinctions and conference prizes.;
6. Experience in one or more of the following areas: stochastic analysis, mathematical finance, statistics, stochastic controls, statistical mechanics, mathematical physics, data analysis, or related subjects.
Desirable:
1. Strong coding skills and expertise in deep learning algorithms;
2. Experience with using deep learning algorithms to process financial data;
3. Demonstrated ability to work in a multidisciplinary team of researchers across mathematical and quantitative finance backgrounds;
4. High degree of competence in writing programs involving financial instruments;