The new ASI Data Science & Data Engineering Fellowship is now available for 2019 January intake. This Fellowship is available for PhD graduates, postdoctoral researchers and experienced software engineers’ transition into a career in data science.
The ASI fellowship was a fantastic opportunity for easyJet to experiment with data science in a low-risk manner. We hired the Fellows that were working with us, and they have been the foundation of the data science team at easyJet. This is a unique programme that has greatly benefitted easyJet.
To unlock the power of data in gaining competitive advantage, we help organisations to make sense of the data, big and small. We believe firmly in providing innovative, simple and easy to implement solutions that generate business value. We offer a comprehensive package of bespoke services include consulting, training and sourcing outstanding data specialists from our very own data science fellows and community.
Applications Deadline: October 15, 2018
Course Level: Fellowship is available for PhD graduates, postdoctoral researchers and experienced software engineers’ transition into a career in data science.
Study Subject: Fellowship is available in the field of Software Engineering.
Scholarship Award: ASI does not provide accommodation. Fellows are paid London Living Wage for four days a week for the duration of their six-week project. The two weeks of training at the beginning of the Fellowship is free of charge for Fellows.
Nationality: This fellowship is available for international students.
Number of Scholarships: Numbers not given
Scholarship can be taken in the UK
Eligibility for the Scholarship:
Eligible Countries: This fellowship is available for international students.
Entrance Requirements: You must have the right to work full time in the UK. If this is on a basis of a visa, please provide a copy of your visa with your application.
In general, successful candidates meet most of the requirements below:
A PhD in a STEM subject.
A high level of mathematical competence.
The ability to code.
Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, natural language processing, optimisation).
Experience with some coding libraries frequently used in data science.
The ability to communicate effectively with different audiences.
Experience composing and following a project plan/sticking to self-imposed deadlines/proactively solving your own problems.
English Language Requirements: Applicants whose first language is not English are usually required to provide evidence of proficiency in English at the higher level required by the University.