Research Assistant in Machine Learning and Quantitative Finance
Oxford-Man Institute of Quantitative Finance (OMI), Eagle House, Walton Well Road, Oxford
Research Assistant in Machine Learning and Quantitative Finance
This post is fixed-term until for 1 year.
Reporting to Professor Álvaro Cartea, the post holder will undertake research at the intersection of machine learning and quantitative finance, working as part of a research team led by Professor Álvaro Cartea and Professor Mihai Cucuringu. The research will focus on the development and application of modern machine learning and statistical methods to problems arising in financial markets, with particular emphasis on large-scale financial datasets and quantitative modelling. The post holder will contribute to the development, implementation and empirical evaluation of new research methodologies, and to the preparation of research outputs for publication and presentation.
You will Hold, or be close to completing, a first degree or integrated Master’s degree in mathematics, statistics, computer science, engineering, or another relevant quantitative discipline, together with relevant research experience.You will also possess sufficient specialist knowledge in the discipline to work within established research programmes.
We proudly hold a Race Equality Charter Bronze Award and a departmental Athena SWAN Silver Award, which guide our progress towards advancing racial and gender equality. As part of our commitment to openness, inclusivity and transparency, we would particularly welcome applications from women and black and minority ethnic candidates who are currently under‑re‑presented in positions of this type at Oxford.
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Research Assistant in Machine Learning and Quantitative Finance in Oxford employer: Corehr
As a leading research institution, we offer an exceptional environment for postdoctoral researchers to thrive, particularly in the vibrant city of Liverpool. Our commitment to collaboration across prestigious universities fosters a dynamic work culture that prioritises innovation and inclusivity, while our hybrid working model supports a healthy work-life balance. With ample opportunities for professional growth and a focus on diversity, we are dedicated to creating a meaningful and rewarding experience for all employees.
