FLAME University Hiring for Research Associate – Computing & Data Sciences at Pune Full Time

FLAME University is the pioneer of liberal education in India, delivering the country’s premier interdisciplinary education experience. Its educational philosophy is rooted in the concept of liberal education, a system of instruction that transcends the artificial divisions that exist between disciplines and unmasks the underlying unity of all knowledge. It urges the student to engage with multiple disciplines in order to view issues from different perspectives for a better understanding of the subject and discover more effective solutions to problems. FLAME University is driven to being one of India’s most respected and reputed centers of learning – the premier destination of choice for higher education in the nation for learners and teachers, to push the design and nature of studies and to create a societal up-gradation phenomenon, particularly in the fields of liberal education and leadership. FLAME has been set up with an idealistic vision and a social commitment to contribute to society nobly.

Applications are invited from meritorious and qualified candidates for the position of Research Associate in the School of Computing and Data Sciences. The position is contractual in nature, for a period of 11 months.

Candidate Qualifications:

We are looking for candidates with a Masters degree in Computer Science, Statistics, Modeling and Simulation, Data Science, Applied Mathematics, Physics, and allied areas. Candidates must have a background of hands-on work in Machine Learning in Python, R, and packages such as Pandas, Scikit Learn, Pytorch etc. Above all, we want the candidate to be comfortable with mathematical and algorithmic ideas, and demonstrated ability to work with new ideas.

What we offer to the candidate:

We are a research group that works on applications as well as fundamentals of machine learning methods. After working with us, the candidate will acquire an in-depth knowledge of the mathematical and algorithmic aspects of machine learning. They will have developed novel methods and published papers, which help them in their future work.