The goal of this job is to examine the limitations of current data-driven decision approaches in Big Data, from scalability and performance points of view, defining new predictive models suitable in these scenarios. To achieve these goals, the current Ph.D. proposal will focus on two more specific directions:
- at the data management layer, we are interested in filling the gap existing in the storage for Big Data, which mainly focuses on binary large objects, by proposing new solutions for the efficient handling of huge volumes of small data objects
- at the application level, we will leverage the above solutions for both small and large objects to validate a set of original predictive models for Big Data analytics and processing
Description
Current data-driven decision approaches do not scale in Big Data scenarios, where the volume, velocity and variety of data make their management and analysis difficult to address.
Like in the case of processing, where new programming models have arisen, novel techniques for efficient and scalable data-driven decision making processes are needed.
The goal of this job is to examine the limitations of current data-driven decision approaches in Big Data, from scalability and performance points of view, defining new predictive models suitable in these scenarios. To achieve these goals, the current Ph.D. proposal will focus on two more specific directions:
- at the data management layer, we are interested in filling the gap existing in the storage for Big Data, which mainly focuses on binary large objects, by proposing new solutions for the efficient handling of huge volumes of small data objects
- at the application level, we will leverage the above solutions for both small and large objects to validate a set of original predictive models for Big Data analytics and processing.
To apply, please email a cover letter, CV, contact address of at least two professional references and copies of degree certificates to Dr. María S. Pérez. Incomplete applications will not be considered or answered.
Nr of positions available : 1
Research Fields
Computer science
Career Stage
Early stage researcher or 0-4 yrs (Post graduate)
Research Profiles
First Stage Researcher (R1)
Benefits
The candidate will be mainly hosted by the Universidad Politecnica de Madrid, where will develop the main work. The candidate will spend a period of 6 months at Inria Rennes, under the supervision of the secondary advisors, to design and implement a platform for the efficient storage and retrieval of many (e.g. billions) of small data objects.
Comment/web site for additional job details
bigstorage.oeg-upm.net/jobs.html
Requirements
| ENGLISH |
| Excellent |
| Computer science |
| Mandatory skills: ideal candidates would hold a B.Sc. or Master's in computer science or related fields and background on Mathematics, good level of English (spoken and written) and ability for cooperative work in a team. Desire skills: Knowledge on Big Data frameworks, knowledge on Storage Systems, Distributed File Systems, knowledge on Data Mining and/or good programming skills. | |
| At the time of recruitment, the applicant must not have lived in Spain for more than 12 month in the previous 36 month and he/she is in the first four years of his/her research career, starting at the date of obtaining the degree which would formally entitle to embark on a doctorate. |
| Master Degree or equivalent |