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Applied Sciences PhD INTERN at Microsoft

Hyderabad, Telangana, India -

Full Time

Start Date

Immediate

Expiry Date

18 Feb, 26

Salary

0.0

Posted On

20 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Analysis, Algorithm Improvement, Research Techniques, Data Quality, Statistical Analysis, Prototyping, Data Preparation, Feature Ideation, Experimental Design, AI Applications, Scalable Systems, Data Modeling, Technical Constraints, Data Review, Problem Solving

Industry

Software Development

Description

Analyze and improve performance of advanced algorithms on large-scale datasets and cutting-edge research in machine intelligence and machine learning applications. Think through the product scenarios and goals, identify key challenges, then fit the scenario asks into Machine Learning (ML) tasks, design experimental process for iteration and optimization. Implement prototypes of scalable systems in AI applications. Gain an understanding of a broad area of research and applicable research techniques, as well as a basic knowledge of industry trends and share your knowledge with immediate team members. Prepare data to be used for analysis by reviewing criteria that reflect quality and technical constraints. Reviews data and suggests data to be included and excluded and be able to describe actions taken to address data quality problems. Assist with the development of usable datasets for modeling purposes and support the scaling of feature ideation and data preparation. Help take cleaned data and adapt for machine learning purposes, under the direction of a senior team member Currently pursuing a Doctorate Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field Must have at least one additional quarter/semester of school remaining following the completion of the internship

Responsibilities

Analyze and improve the performance of advanced algorithms on large-scale datasets. Assist with the development of usable datasets for modeling purposes and support the scaling of feature ideation and data preparation.