M.Tech RA/RAP Admissions 2024

(Last updated on 23/06/2024)

Please follow this page for updates on MTech RA/RAP Admissions to IEOR Department for the 2024 July Semester. Links to the online-registration form for the test, interview meeting links, and other information etc will be posted here. 

Information for RAP Applicants

Several positions are available for 3-Year MTech for Research Assistants through Projects (RAP) candidates. These positions are supported through the newly created IITB-FedEx  Centre for Advanced Logistics and Analytics (FedExALFA), and other projects being undertaken in the department. These projects are in the domains of logistics, supply-chains, operations, digitalization, applications of data-analytics and AI/ML in IEOR and related topics. More specific details about the projects will be updated shortly.

Updates (19/06/2024): Please see below for a description of some of the projects. More projects may be announced shortly.

Minimum Criteria for candidates to be considered for RA/RAP Admissions: 

Applicants should have applied for M.Tech Program at IEOR, IIT Bombay under RA category. All candidates who meet the above criteria can register for the interview process. You will be required to fill in a few personal and academic details. Please see the schedule below for the link to the form. Registered candidates would then attempt an aptitude test and submit their solutions. Out of all candidates who register and submit their aptitude test solutions, at most 60 (or less) will be shortlisted for interview based on their GATE score only. Final shortlisting for admission will be based on interview score which will consider the aptitude test performance also. 

Schedule

Please check below the rules and regulations for RA/RAP position.

• RA and RAP are fulltime MTech for duration of 3 years.

• Candidates admitted under RA/RAP category shall not accept or hold any appointment (paid or otherwise) or  receive any emoluments, salary, stipend from any other source during RAP tenure.

• The continuation of RA/RAP assistantship  will be subject to monthly attendance and satisfactory academic performance

• Candidates admitted under RAP category, against a sponsored R&D project, will be required to work for the same project for 20 hours per week and undertake the M.Tech. dissertation work under the same project investigator(s), and will be considered for a financial assistantships from the sponsored project as per the norm for a period of 36 months.

• Candidates admitted as RA will be considered for financial assistantships of Rs.13,400 (per month), along with annual increments as prescribed by Institute rules, for a maximum period of 36 months subject to serving as a research assistant in a course/laboratory for 20 hours per week as assigned by the concerned academic unit.

• The assistantship will be paid on the basis of monthly attendance.  Assistantships supported through certain projects for RAPs may be higher than the above stipend.

• Employees on the rolls (with or without pay) of any organization are not eligible for admission under RA/RAP category.

• A valid GATE score is mandatory; the interview (and aptitude test) are mandatory for admission in IEOR.

• See also detailed rules and information about admissions to MTech Programmes in IIT Bombay.

Contact

Please send an e-mail to admissions@ieor.iitb.ac.in or office.ieor@iitb.ac.in regarding any specific queries on M.Tech. RAP/RA Admissions. 

List of Projects

1. [RAIL] Planning of railways/metro-rail: The M.Tech. RA Position in the Railway project broadly involves participation in the railway timetabling tasks that the railway project team takes up for the Indian Railway: both long-distance trains and suburban-rail/metro-rail services. The project involves analysis, interpretation of data (often received from the Indian railways). The project also involves development of software tools for timetabling, railway-crew-allocation and other regular allocation activities of the suburban-rail/metro-rail and long-distance (passenger, freight) trains. In addition to keen interest in the above tasks, programming acquaintance is very welcome. Details of the past and ongoing projects in railway/metro-rail activities by the team are listed at: https://www.ee.iitb.ac.in/%7Ebelur/railways Candidates are encouraged to visit the page to know the type of efforts pursued by the team. 
   Associated faculty: Narayan Rangaraj and Madhu Belur
    
2. [WIAI] AI/ML techniques in next generation wireless networks: 5G/6G communication systems are envisioned to will operate in Giga and Tera Hertz frequency range. In this frequency range, long distance communication is not possible due to high attenuation and absorption. Hence energy needs to focuses in sharp beams to achieve good throughput. The project will explore how to use AI/ML techniques for optimal beam formation to improve network performance. 
   Associated faculty: Manjesh Hanawal and Prasanna Chaporkar
    
3. [SUWI] Sustainable next generation wireless networks: AI-ML based solutions are being widely used and explored for optimizing network resources and enhancing performance metrics for various network operations; few examples are load balancing, adaptive security, and interference management. Our goal in this project is to develop algorithms for resource allocation, load balancing, adaptive security, mobility and interference management in the network with aim of achieving joint optimization of network goals and energy utilization. Energy usage of network resources should be taken as consideration while deciding goals for algorithms along with other QoS requirements. However, challenge is how to model energy consumption of various network functions which we also plan to explore as part of this project (as detailed in previous section).
   Associated faculty: Manjesh Hanawal and Prasanna Chaporkar
    
4. [SYSAD] RA for IEOR system administration: Responsibilities include maintaining IEOR computational and web servers, upgrading hardware, installing & updating software, maintaining the department website, supporting students, staff and faculty in using these systems, assisting in maintaining other systems including printers, wifi, scanners and switches etc. The candidate should be comfortable using and managing computers and other hardware. Familiarity with Linux, good communication skills, analytical and debugging abilities, basic knowledge of networking are desirable.
    
5. [LABS] RA for IEOR labs: Responsibilities include assisting IEOR Faculty with developing UG program lab curriculum for Digital Enterprise Systems and other labs. Familiarity with digital, electrical and mechanical systems will be preferable.
    
6. [IMGSC] Image processing applications in supply chain and security: In this project we will address image process problmes under challenging scenarions like images taken under fog, low resolutions, etc.
   Associated faculty: Manjesh Hanawal
    
7.  [DIGIT] Digitalisation of SC/operations: Digitalisation of supply chain/ logistics/ manufacuring operations is expected to provide immense potential for improved decisions making. This project aims to create a appropriate digitialisation of some operations related with services/ manufacturing; to explore the use of various sensors / IoT devices towards data collection and digital representation.  Looking for student with strong interest in hardware, use of raspberry-pi/ Arduino boards, etc. The RA is also expected to work on interesting decision-making and simulation based projects related with logistics.
   Associated faculty: Jayendran Venkateswaran
    
8. [3DPAC] Novel reinforcement learning based algorithms  and Generative AI based mixed reality toolkit for online convex and non-convex 3d packing problem: The project involves development of suitable Reinforcement Learning techniques for online non-convex 3d packing problem. Online 3d packing problem aims at arrangement of items spatially where the items arrive dynamically and the arrangement of a previous item is unaltered by the arrival of the subsequent items. Typically the items are assumed to be cubical or cuboidal, with predefined shapes, sizes and weights and the configuration resulting from the online 3d packing algorithm should satisfy certain constraints related to the physical stability of the packing,  orientations of the packed items and relative orientations of adjoining items. Though this problem has been heavily investigated using optimization techniques, heuristics and learning based methods, it's extension to handle objects with non-convex shapes and geometry
   is relatively less explored. In this project, we will investigate novel reinforcement learning algorithms to tackle online 3d packing with items of non-convex geometries, subject to constraints based on orientations (both individual and relative), stability and others. The developed techniques will be tested based on simulated and real datasets. Theoretical tools for the proposed techniques will also be developed.
   
   We also aim to develop an interactive toolkit that can aid users to understand and interact with 3d packing situations, which can help a learning based algorithm to improve its solutions, when the solution prescribed by the algorithm might be deemed less desirable due to practical reasons (legal, geographical, physical etc). The interactive toolkit will involve development of generative AI based techniques for designing, manipulation and visualization of all the intermediate steps in the process. The mixed reality feature aims to provide a user friendly environment which would help in seamless user involvement in the overall packing algorithmic process. This toolkit will help in creating quicker testbed for testing out newer approaches to the packing problem . Initially 3d packing with cubical and cuboidal structures will only be considered. Extensions to different types of objects will also be explored.
   Associated faculty: P Balamurugan
    
9. [PLATO] Planning for platooning and other advances based on automation of driven vehicles in logistics: When two or more trucks move one following the other in a line over a long distance, it is called platooning. Platooning offers some savings in the fuel cost as trucks following the first one experience lesser air resistance. Significant effects are however possible if the trucks following the leader are running in autonomous mode. Drivers' can be relieved for some durations, for example. Platooning also affects the safety of the operations depending on the technical aspects of the automation involved. We analyse the technologies available in this domain and the effects it may have on transportation costs, crew operations and safety.
   Associated faculty: Ashutosh Mahajan