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Applied Scientist Core Maching Learning Intern


Cambridge, United Kingdom

The Core Amazon Machine Learning team in Cambridge develops innovative machine learning methods for the modeling and analysis of complex data. We collaborate closely with other science, engineering and product teams in Amazon in several application domains such as robotics, natural language understanding and many more. The particular research areas of the group are uncertainty quantification, data-efficient learning, streaming applications and privacy aware and deep learning. We focus on the mathematical and computational challenges that arise in these topics.

We are recruiting three (3) curious and creative machine learning scientist interns who are prepared to learn new skills and who are willing to collaborate with scientists and engineers to implement new machine learning methods. (Note: this job posting is one out of three identical postings.)

The internships will involve working on the deployment of innovative machine learning algorithms for the modeling and analysis of data. The candidates will be expected to work in research areas such as probabilistic modeling, meta-modeling, transfer learning and deep learning. Challenges will involve dealing with very large data sets and design constraints that may arise from, for example, hardware implementation or latency requirements. On the other end of the spectrum, challenges can also include the potentially small number of labelled data.

· Current enrollment in a degree-granting college or university working towards a PhD in Machine Learning, Data Mining, Statistics, Applied Mathematics, or a related field.
· Hands on experience in predictive modelling and analysis, in particular one or more of: Bayesian probabilistic modeling, deep learning, transfer learning, meta-learning, unsupervised feature learning.
· At least one refereed academic publication in these areas.
· Good coding skills. Experience in Python is a plus.
· Good communication skills and the ability of working in a team.

· Ability to convey rigorous mathematical concepts and considerations to non-experts.
· Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.
· Strong software development skills.
· Familiarity with Bayesian methods such as Gaussian processes.


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