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M.Tech in Biomedical Data Sciences

A two-year postgraduate programme at the intersection of life sciences and data science.

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M.Tech in biomedical data sciences overview 

The programme integrates biological sciences with data science to prepare graduates to analyse complex biomedical data for research and healthcare applications. Students experience:

Biomedical data foundations

Learning in statistics, computational biology, programming and data management for biomedical datasets.

Machine learning in healthcare

Application of machine learning to clinical trials, biomedical imaging and healthcare decision-making.

Hands-on work with real data

Workshops using genomics, imaging and digital health data, from cleaning to interpretation.

Interdisciplinary research exposure

Systems biology, network modelling and research projects focused on real biomedical problems.

Programme details

About

The M.Tech in Biomedical Data Science provides an interdisciplinary curriculum designed to train students to analyse and interpret large, complex biomedical datasets from multiple sources. The programme equips students with the skills required to solve challenging problems in healthcare and life sciences through advanced data analysis and computational approaches.

The Centre for Life Sciences at Mahindra University is developing a collaborative research and teaching environment where students and faculty work together to create innovative technologies and analytical methods. These efforts aim to improve disease diagnosis and treatment while reducing healthcare costs through data-driven insights.

Why M.Tech in Biomedical Data Science?

The rapid growth of biomedical data has created a strong demand for professionals capable of analysing and interpreting this information. As noted by experts in the field, the increasing availability of biomedical datasets requires highly skilled data scientists who can translate data into meaningful improvements in healthcare.

Over the past decade, biomedical data has expanded significantly due to advancements in:

  • Large-scale genomic sequencing
  • Medical imaging technologies
  • Mobile health (mHealth) data
  • Clinical and electronic health records

Simultaneously, advances in computing power and storage have made it possible to analyse this data using advanced statistical techniques, machine learning models and biological simulations. These developments have led to the emergence of biomedical data science as a critical field that integrates biology, medicine, statistics and computing.

While leading universities globally already offer specialised programmes in biomedical data science, such programmes are still rare in India. This programme aims to address that gap by training the next generation of professionals capable of advancing biomedical research and healthcare innovation.

Through this programme, the Centre for Life Sciences seeks to develop expertise in biomedical data science and support research across areas ranging from basic biological research to clinical investigation.

CourseL-T-PCredits
Computational Biology3-0-03
Statistics for Biomedical Data Science3-0-24
Python Programming2-0-23
Linux Workshop0-0-21
Data Management and Engineering3-0-03
Workshop in Data Visualization0-0-21
CourseL-T-PCredits
Machine Learning for Biomedical Data Science3-0-03
Clinical Trials: Design & Analysis3-0-03
Algorithms in Biomedical Data Science3-0-03
Biomedical Imaging3-0-03
Workshop in Genomics Data Analysis0-0-21
Elective I3-0-03
CourseL-T-PCredits
Digital Health Informatics3-0-03
Systems Biology & Network Modeling2-0-23
Research Project – I0-0-126
Elective II3-0-03
CourseL-T-PCredits
Research Project – II0-0-3216

Eligibility

Applicants may hold any of the following degrees:

  • B.E. / B.Tech, B.Sc. (Engineering), B.Sc. (four-year programme), M.Sc., M.C.A., MBBS, BDS, B.Pharm., B.V.Sc.

Minimum academic requirement

Applicants must have:

  • Minimum 60% aggregate marks, or
  • First-class qualification as defined by the awarding university, or
  • CGPA / CPI of at least 6.0 on a 10-point scale, or an equivalent grade under other grading systems.

MU test syllabus

Potential employers

Graduates of the programme may find opportunities across research organisations, healthcare companies, pharmaceutical firms, consulting companies and technology service providers.

Core research and pharmaceutical companies

  • Eli Lilly
  • AstraZeneca
  • Takeda
  • Pfizer
  • Merck
  • Johnson & Johnson
  • Novartis
  • Corteva Agrisciences
  • Sanofi
  • Bristol Myers Squibb
  • GSK
  • Novo Nordisk
  • Abbott
  • Siemens Healthineers
  • Boehringer Ingelheim

Research services and analytics companies

  • Clarivate
  • Elucidata
  • Nimble Clinical Research
  • IQVIA
  • Evalueserve
  • LabCorp
  • Quantium
  • WNS
  • Cardinal Health
  • Syneos Health
  • US Pharmacopeia
  • Axtria
  • Ingenious Insights
  • Caidya

IT and technology services companies

  • Tech Mahindra
  • Wipro
  • TCS
  • Infosys
  • Cognizant
  • Persistent
  • Accenture
  • HCLTech

Note: The list is indicative and not exhaustive.

FAQs

All core courses and projects are anchored in biomedical and healthcare data rather than generic applications.

Yes. The curriculum is designed to support both biology-focused and engineering-focused learners.

Students work with genomics, imaging, clinical trial and digital health datasets.

Graduates pursue roles in biomedical data science, bioinformatics, health analytics and research.

Yes. The final year focuses on research projects and thesis work, supporting doctoral pathways.

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M.Tech in Autonomous Electric Vehicles

A two-year postgraduate programme focused on electric vehicles and intelligent mobility systems.

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Programme curriculum

M.Tech in autonomous electric vehicles overview 

The programme prepares engineers to work across electric vehicles and intelligent mobility by integrating powertrain systems with embedded intelligence, connectivity and autonomous technologies. Students experience:

EV powertrain fundamentals

Core learning in power electronics, electric drives, batteries and powertrain systems.

Embedded and vehicular systems

Hands-on work with embedded automotive platforms and vehicular communication technologies.

Vehicle intelligence and autonomy

Application of sensors, connectivity and AI algorithms to autonomous driving problems.

Industry exposure

Internships and industry interaction embedded into the programme structure.

Programme details

Expected programme outcomes

Graduates of this programme will be able to:

  • Understand the various components of an electric vehicle and their integrated functioning.
  • Conceptualise, design and implement electric drive systems for automobiles along with the associated electronic circuitry.
  • Analyse and design battery management systems (BMS) for electric vehicles.
  • Understand the role of intelligent systems in modern automobiles.
  • Develop intelligent mechanisms to improve vehicle performance and operation.
  • Design and implement intelligent transport systems (ITS) with vehicular and infrastructure-based communication.
  • Conceptualise and analyse autonomous vehicle systems.

Programme highlights

100% internship opportunities for admitted students

Highest stipend offered: ₹60,000 per month

Eligibility

  • A full-time bachelor’s degree from a recognised university or institute with a minimum aggregate of 60% marks or equivalent grade.
  • Candidates appearing for their final semester examination are also eligible to apply.
  • B.E./B.Tech. in Electrical Engineering, Electrical and Electronics Engineering, Electronics and Communication Engineering, Instrumentation Engineering, Mechanical Engineering, Automobile Engineering or Mechatronics***

And

  • A valid GATE score in Electrical Engineering, Electronics and Communication Engineering, Instrumentation Engineering or Mechanical Engineering is mandatory.

*Applicants with degrees in Mechanical Engineering, Automobile Engineering or Mechatronics may be required to complete a bridge course before the start of the programme.

Admission process

  • GATE-qualified candidates: Applicants with a valid GATE score and a percentile of 80 or above will be shortlisted for an interview.
  • Non-GATE candidates: Applicants without a valid GATE score, or with a percentile below 80, must appear for a written test conducted by ECSE–MU, followed by an interview for shortlisted candidates.

Fee structure

Tuition fee: ₹1,00,000 per annum

Hostel stay: Not mandatory

Laboratory facilities

  • Power electronics and machines laboratory
  • Embedded systems for automobiles laboratory
  • Vehicular communication networks laboratory
  • Battery management systems and controls laboratory

FAQs

It integrates electric powertrains, embedded systems, connectivity and autonomy within a single curriculum.

Students work with EV power electronics, embedded automotive systems and vehicular communication platforms.

Yes. Bridge support is provided to strengthen electrical and electronics fundamentals where required.

Through internships, industry interaction and applied project work aligned to current mobility technologies.

Yes. Advanced coursework and a strong thesis component prepare students for doctoral study and R&D roles.

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M.Tech in Artificial Intelligence & Data Science

A two-year postgraduate programme focused on AI and data science, enabling graduates to design intelligent systems and extract insight from complex, large-scale data.

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M.Tech in artificial intelligence & data science overview 

The programme develops strong foundations in artificial intelligence, data science and high-performance computing, progressing towards advanced techniques used in real-world AI systems. Students experience:

Strong AI and data foundations

Advanced learning in databases, machine learning, mathematics for AI and core artificial intelligence concepts.

Hands-on work with real data

Practical coursework in big data analytics, computer vision, natural language processing and deep learning.

Domain-focused specialisation

Electives that connect AI with application areas such as bioinformatics, communications and smart industry.

Research and industry exposure

An industry internship, project-based learning and a two-stage dissertation on real-world AI problems.

Eligibility criteria
Fee structure
Important dates

Programme details

Objective

The M.Tech in Data Science and Artificial Intelligence programme is designed to train graduates from computer science and engineering, electronics and communication engineering, electrical and electronics engineering, or Artificial Intelligence backgrounds to develop advanced expertise in artificial intelligence and data science.

The programme combines theoretical foundations with practical applications to help students build strong capabilities in emerging technologies and analytical methods.

During the first two semesters, students study core subjects such as databases, big data analytics and high-performance computing (HPC). The curriculum also introduces key areas including natural language processing (NLP) and digital image processing, followed by advanced topics such as computational sequence modelling and deep learning. These areas form the foundation for developing professional competence in artificial intelligence.

Students are also introduced to reinforcement learning, with applications in autonomous systems, as well as financial risk management, where data analytics plays an increasingly significant role.

The programme offers the option to specialise in one of the following streams:

  • Bioinformatics
  • Communications
  • Smart industry

Each stream includes a foundational course followed by an advanced course.

Graduates of the programme are expected to develop both the motivation and expertise to pursue careers in the rapidly evolving fields of artificial intelligence and data science, or continue into doctoral research programmes in related areas.

  • Total credits: 63
  • Prior degree required: B.Tech in CSE, AI, ECE or EEE

Note: The extension of the work visa period for students pursuing a master’s degree in France has been increased from 2 years to 5 years.

Academic structure

Our academic structure is designed to establish robust foundations, followed by increasing specialization in later years.

  • Credit structure: Total of 62 credits
  • Duration: 2 years / 4 semesters
  • Coursework is covered in Semesters 1 and 2, with project, seminars, internship and thesis across Semesters 3 and 4.
CourseL-T-PCredits
Advanced Engineering Mathematics3-1-04
Foundations of Artificial Intelligence3-0-24
Probability & Random Processes3-1-04
Advanced Data Structures & Algorithms3-0-24
AI & Data Science Lab0-0-42
Research Methodology2-0-02
CourseL-T-PCredits
Machine Learning3-1-04
Deep Learning3-0-24
Big Data Analytics3-0-24
Statistical Learning & Data Mining3-0-03
Advanced AI Lab0-0-42
Program Elective I3-0-03
CourseL-T-PCredits
Program Elective II3-0-03
Program Elective III3-0-03
Seminar0-0-21
Dissertation – Phase I0-0-126
CourseL-T-PCredits
Dissertation – Phase II0-0-2412

FAQs

This programme focuses specifically on AI and Data Science, with all core courses aligned to machine learning, data systems and intelligent applications.

Through applied coursework, modern tools, an industry internship and a final-year dissertation.

Yes. The curriculum begins with foundational courses before progressing to advanced topics and specialisations.

Yes. Advanced electives and a substantial dissertation prepare students for doctoral study and research roles.

Graduates pursue roles such as data scientist, machine learning engineer, AI researcher and related positions across technology and research organisations.

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M.Tech in Advanced Wireless Communication

A two-year postgraduate programme focused on advanced wireless systems, enabling engineers to design, simulate and optimise 4G, 5G and emerging 6G communication technologies.

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M.Tech in advanced wireless communication overview 

The programme prepares students to work across modern and next-generation wireless communication systems, covering physical and MAC layers, networks and optimisation. It combines strong theoretical foundations with hands-on system design, simulation and experimentation aligned with international wireless standards. Students experience:

Foundation in next-generation wireless

4G, 5G and emerging 6G concepts through digital and wireless communication, RF and microwave engineering.

Hands-on communication labs

Industry-relevant labs using USRPs, MATLAB simulators, and AI/ML platforms for link- and system-level implementation.

AI-driven and secure networks

Courses in AI/ML for communications and wireless security focused on intelligent, resilient network design.

Advanced specialisation and research

Electives in MIMO, ISAC, vehicular networks, IoT, antennas and coding, culminating in a research-led thesis.

Programme details

Academic structure

Our academic structure is designed to establish robust foundations, followed by increasing specialization in later years.

  • Credit structure: Total of 62 credits
  • Duration: 2 years / 4 semesters
  • Coursework is covered in semesters 1 and 2, with project, seminars, internship and thesis across Semesters 3 and 4.
Figure 1. Evolution of wireless communication technologies
Figure 2. Research aspects that bring AI technologies into beyond 5G wireless networks

Programme outcomes

Graduates of the programme will be able to:

  • Understand the principles of next-generation telecommunications systems based on evolving user requirements.
  • Design, model and implement energy-efficient wireless communication systems and modern telecommunication standards.
  • Integrate artificial intelligence and machine learning techniques with wireless communication systems.
  • Apply advanced communication technologies to develop multi-protocol network architectures.
  • Design and analyse communication links and systems for next-generation networks.
CourseL-T-PCredits
Advanced Engineering Mathematics3-1-04
Advanced Digital Communication3-1-04
Probability & Random Processes3-1-04
RF & Microwave Engineering3-0-24
Wireless Communication Lab0-0-42
Research Methodology2-0-02
CourseL-T-PCredits
MIMO & Massive MIMO Systems3-1-04
5G & Beyond Wireless Systems3-1-04
Machine Learning for Wireless Communication3-0-24
Advanced Antennas & Propagation3-0-03
Advanced Wireless Lab0-0-42
Program Elective I3-0-03
CourseL-T-PCredits
Program Elective II3-0-03
Program Elective III3-0-03
Seminar0-0-21
Dissertation – Phase I0-0-126
CourseL-T-PCredits
Dissertation – Phase II0-0-2412

Eligibility

  • B.E./B.Tech. in Electrical Engineering, Electrical and Electronics Engineering, Electronics and Communication Engineering, Telecommunication Engineering, Communication and Information Systems, Computer Science and Engineering, Instrumentation Engineering, Electronics and Biomedical Engineering, or Electronics and Computer Engineering.
  • A valid GATE score in Electrical Engineering, Electronics and Communication Engineering or Instrumentation Engineering is mandatory.
  • Candidates appearing for their final semester examination in the current year are also eligible to apply, provided they submit a valid GATE score.

Admission process

  • GATE-qualified candidates: Applicants with a valid GATE score and a percentile of 70 or above will be invited for an interview as part of the admission process.
  • Non-GATE candidates: Applicants without a valid GATE score, or with a percentile below 70, must appear for a written test conducted by the École Centrale School of Engineering, Mahindra University, followed by an interview for shortlisted candidates.

FAQs

This programme focuses specifically on advanced wireless systems aligned with 4G, 5G and emerging 6G standards, rather than broad analogue or mixed communication themes.

Students work with USRPs, MATLAB wireless toolchains and AI-based communication labs to design, simulate and validate link-level and system-level models.

It is suited for graduates in Electrical, Electronics, Communication, Telecommunication, Computer Science, Instrumentation or related disciplines with an interest in wireless systems and networks.

Yes. The curriculum and tools are aligned with industry expectations of major telecom vendors, chipset companies and network operators.

Yes. The strong theoretical foundation, advanced electives and substantial thesis prepare students well for doctoral research and advanced research roles.

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