ASI Data Science and Data Engineering Fellowships 1024 views

ASI Data Science and Data Engineering Fellowships for Software Engineers.

The new ASI Data Science and Data Engineering Fellowship is available for 2019 January intake. These Fellowships is are available for PhD graduates, postdoc researchers and experienced software engineers’ transition into a career in data science.

ASI fellowships are a fantastic opportunity for researchers/professionals to experiment with data science in a low-risk manner. This is a unique program that has greatly benefitted recipients internationally.

Objectives

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.

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.

Eligibility conditions:

Entrance Requirements: the candidate 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.

Successful candidates meet most of the requirements below:

1. A PhD in any of the STEM subject.
2. A high level of mathematical competence.
3. Programming experience.
4. The ability to code.
5. Some experience with theoretical concepts of statistical learning (e.g. hypothesis testing, Bayesian Inference, Regression, SVM, Random Forests, Neural Networks, natural language processing, optimization).
6. Experience with some coding libraries frequently used in data science.
7. The ability to communicate effectively with different audiences.
8. 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.