Research Associate in Machine Learning
Applications are invited for the position of Research Associate to be held jointly with the Department of Computing and the Department of Mathematics. The research associate will work on the theoretical foundations of stable machine learning algorithms, and deep neural networks in particular. The research associate will work on the theoretical foundations of stable machine learning algorithms, and deep neural networks in particular. ...
Job listing information
- Reference ENG00790
- Date posted 1 April 2019
- Closing date 30 April 2019
Applications are invited for the position of Research Associate to be held jointly with the Department of Computing and the Department of Mathematics. The research associate will work on the theoretical foundations of stable machine learning algorithms, and deep neural networks in particular. The research associate will work on the theoretical foundations of stable machine learning algorithms, and deep neural networks in particular.
Breakthroughs in modern Neural Network (NN) architectures and related algorithms in Machine Learning (ML) have entirely transformed whole areas of computer science such as computer vision and natural language processing. Recent research papers, propose to use deep neural networks to solve otherwise intractable problems in finance such as pricing and hedging in high dimensions and to model price formation using high-frequency data. Unfortunately, both theoretical and empirical results have shown that neural networks compute unstable classifiers. An unstable classifier is vulnerable to adversarial attacks and illegal exploitation. A necessary condition for successful ML systems in real-world applications, especially ones in the financial sector, is that the underlying system is stable. Without resolving this challenging problem, it is not possible to make meaningful progress in critical application areas such as the explainability and interpretability of machine learning algorithms, or efficient and robust training methods for reinforcement learning.
Duties and responsibilities
The Research Associate will work directly with Dr. P. Parpas, Dr. N. Kantas, Professor G.A. Pavliotis at Imperial College and the AI research Team in JP Morgan. They will join a large, vibrant research team working on problems related to machine learning, optimization, stochastic optimal control, partial differential equations, quantitative finance, statistical mechanics and stochastic differential equations.
- A PhD (or equivalent) in Mathematics, Statistics, Computer Science, Engineering, Physics, or a related area, or an equivalent level of professional qualifications.
- Excellent record of accomplishment in carrying out original research of high quality in at least two of the following areas: optimization, machine learning, dynamical systems, optimal control, numerical analysis, or stochastic processes.
- Strong programming skills.
- Excellent oral and written communication skills.
- Able to organise your work with minimal supervision and prioritise work to meet deadlines.
*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £33,397 - £37,486 per annum.
In addition to completing the online application, candidates should attach:
- A full CV
- A two-page research statement indicating what you see are interesting research issues relating to the above post and why your expertise is relevant.
Should you have any queries regarding the application process please contact Jamie Perrins: firstname.lastname@example.org
For technical issues when applying online please email email@example.com
The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/
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