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PH-D in Electrical Engineering at Indian Institute of Science
Bengaluru, Karnataka
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About the Specialization
What is Electrical Engineering at Indian Institute of Science Bengaluru?
This Ph.D. Electrical Engineering program at Indian Institute of Science, Bengaluru, focuses on advanced research and fundamental contributions across core and emerging areas of Electrical Engineering. Leveraging IISc''''s renowned research ecosystem, the program addresses critical challenges in power, electronics, communication, control, and computing. It is distinguished by its emphasis on interdisciplinary research, cutting-edge laboratories, and a strong theoretical foundation, preparing scholars for leadership roles in academia and R&D.
Who Should Apply?
This program is ideal for highly motivated individuals holding a Master’s degree in Electrical Engineering or related disciplines, or exceptional Bachelor''''s degree holders seeking to push the boundaries of knowledge. It caters to aspiring researchers, future faculty members, and innovators aiming for high-impact R&D careers in industry. Candidates with strong analytical skills, a passion for problem-solving, and a clear vision for their research trajectory are particularly well-suited.
Why Choose This Course?
Graduates of this program can expect to pursue impactful careers as research scientists, university professors, or lead engineers in R&D divisions of top Indian and multinational companies. Career paths include roles in chip design, power grid modernization, AI/ML research, advanced communication systems, and robotics. Doctoral degree holders typically command premium salaries in the Indian market, with entry-level research roles starting from INR 15-25 LPA and significantly higher for experienced professionals.
Student Success Practices
Foundation Stage
Master Foundational & Advanced Coursework- (Semesters 1-2 (or first 12-18 months))
Actively engage with chosen graduate-level courses, focusing on deep conceptual understanding rather than rote learning. Participate in discussions, solve challenging problems, and effectively present coursework. This rigorous academic foundation is crucial for any advanced research endeavor.
Tools & Resources
IISc Digital Learning Centre, Departmental course material repositories, NPTEL advanced modules, Collaborative peer study groups
Career Connection
Strong coursework forms the bedrock for innovative research, enabling students to identify and formulate novel research problems and develop robust solutions, crucial for R&D roles in both academia and industry.
Engage with Research Groups Early- (Semesters 1-2 (or first 12-18 months))
Identify potential research advisors and actively participate in lab meetings, reading groups, and ongoing projects. This exposes students to active research problems, methodologies, and the unique research culture of IISc, fostering early integration.
Tools & Resources
Departmental faculty profiles and research lab websites, IISc research seminars and workshops, Discussions with senior Ph.D. students and postdocs
Career Connection
Early engagement helps in selecting a suitable research area and advisor, building initial research skills, and demonstrating proactive engagement, which is valued by future employers or collaborators.
Prepare Rigorously for Comprehensive/Qualifying Exams- (End of Year 1 / Start of Year 2)
Dedicate significant time to consolidate knowledge from coursework and broader EE fundamentals. Form study groups, practice past exam papers (if available), and seek guidance from faculty/mentors to build confidence and readiness for this critical milestone.
Tools & Resources
IISc library resources and reference textbooks, Departmental qualifying exam guidelines, Peer study networks and faculty office hours
Career Connection
Successfully clearing qualifiers demonstrates a strong command of fundamentals and analytical rigor, a key trait highly valued in both academic and industrial research settings, indicating readiness for advanced research.
Intermediate Stage
Develop a Robust Research Proposal- (Semesters 3-5 (or Year 2 to mid-Year 3))
Formulate a clear, original, and impactful research problem, conduct an exhaustive literature review, and propose a viable methodology with expected outcomes. Seek continuous feedback from the advisor and expert peers to refine the proposal''''s scope and feasibility.
Tools & Resources
Research databases (Scopus, Web of Science, IEEE Xplore), Academic writing tools (e.g., LaTeX), Presentation software and mock presentation sessions, Mentorship from advisor and advisory committee
Career Connection
A well-articulated proposal showcases problem-solving, critical thinking, and communication skills, which are essential for securing research grants, leading projects, and effectively presenting complex ideas in any R&D role.
Master Specialized Tools and Techniques- (Semesters 3-5 (or Year 2 to mid-Year 3))
Acquire proficiency in domain-specific simulation tools (e.g., MATLAB, ANSYS, Cadence, NS-3), programming languages (e.g., Python, C++), and experimental setups relevant to the research. This practical expertise is vital for executing research effectively.
Tools & Resources
University software licenses and high-performance computing clusters, Online tutorials and certifications (Coursera, edX), Lab workshops and collaboration with advanced users, Departmental technical support
Career Connection
Hands-on expertise with industry-standard tools makes graduates immediately valuable in R&D roles, accelerating their contribution to complex projects and enhancing their marketability in specialized fields.
Actively Publish and Present Research- (Semesters 3-5 (or Year 2 to mid-Year 3))
Aim to publish initial research findings in peer-reviewed conferences and journals. Present work at internal seminars, workshops, and national/international conferences to gain feedback, build a professional network, and establish research credibility.
Tools & Resources
Journal submission platforms (e.g., IEEE Xplore, ACM Digital Library), Conference proceedings databases, IISc internal seminar series, Faculty network for potential collaborations
Career Connection
Publications and presentations enhance academic visibility, demonstrate research productivity, and are crucial for academic job applications, postdoctoral positions, and career progression in research-intensive industries.
Advanced Stage
Focus on High-Impact Thesis Research- (Semesters 6+ (or Year 4 onwards))
Deepen expertise in the chosen research domain, aiming for significant original contributions that advance the state-of-the-art. Continuously iterate on experiments, simulations, and theoretical analysis, ensuring robustness, novelty, and clear articulation of impact.
Tools & Resources
Advanced lab equipment and specialized software, High-performance computing resources (e.g., SERC at IISc), Collaboration with domain experts and international researchers, Dedicated blocks of uninterrupted research time
Career Connection
A high-impact thesis demonstrates profound expertise and the ability to solve complex, unsolved problems, making graduates highly sought after for leadership roles in R&D, specialized academic positions, and entrepreneurial ventures.
Network Strategically & Seek Collaborations- (Semesters 6+ (or Year 4 onwards))
Attend workshops, conferences, and industry events to connect with leading researchers, potential collaborators, and recruiters. Actively engage in discussions and seek opportunities for inter-institutional or industry-academic projects to broaden your research perspective.
Tools & Resources
Professional organizations (IEEE, IET, ACM), LinkedIn and other professional networking platforms, Research symposia and career fairs, IISc alumni network and faculty connections
Career Connection
A strong professional network is invaluable for discovering job opportunities, postdoctoral positions, securing future collaborations, and gaining insights into emerging industry trends and research directions.
Prepare for Post-PhD Career Paths- (Final 1-2 years of the program)
Tailor thesis writing and presentation skills for specific career goals (e.g., academic job market, industrial R&D). Practice interview skills, prepare a compelling CV/portfolio, and seek career counseling from the institution to align your skills with market demands.
Tools & Resources
IISc Career Development Centre services, Mock interview sessions with faculty or industry professionals, Faculty advice on academic versus industrial career paths, Participation in industry career fairs and recruitment drives
Career Connection
Strategic career planning ensures a smooth transition post-PhD, maximizing opportunities for securing desired roles in academia, research institutions, or advanced industry positions that leverage the depth of your doctoral research.
Program Structure and Curriculum
Eligibility:
- Master’s degree (M.E./M.Tech./M.Sc.(Engg.)/M.S. or equivalent) OR Bachelor’s degree (B.E./B.Tech. or equivalent) with high academic record and valid GATE/NET-JRF. Minimum 60% aggregate marks/CGPA 6.75 out of 10 in qualifying degree. Specific prerequisites may apply based on department.
Duration: Typically 3-5 years (variable based on individual progress and research area)
Credits: 12-24 credits (for coursework component, variable based on prior academic background and entry scheme) Credits
Assessment: Internal: undefined, External: undefined
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EE 201 | Probability and Random Processes | Core Elective | 3 | Probability spaces and axioms, Random variables and distributions, Stochastic processes, Estimation theory basics, Hypothesis testing fundamentals |
| EE 202 | Linear Algebra and Optimization | Core Elective | 3 | Vector spaces and linear transformations, Eigenvalue problems and matrix decompositions, Convex sets and functions, Unconstrained optimization methods, Constrained optimization and KKT conditions |
| EE 204 | Digital Signal Processing | Core Elective | 3 | Discrete-time signals and systems, Z-transform and DFT/FFT, FIR and IIR filter design, Multi-rate signal processing, Adaptive filtering algorithms |
| EE 206 | Computer Architecture | Elective | 3 | Instruction set architecture, Pipelining and parallelism, Memory hierarchy and caching, Multicore and GPU architectures, Interconnection networks |
| EE 213 | Deep Learning | Elective | 3 | Neural network architectures, Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Backpropagation and optimization, Transfer learning and fine-tuning, Generative models (GANs, VAEs) |
| EE 216 | Modern Control Systems | Elective | 3 | State-space analysis and representation, Controllability and observability, Pole placement and observers, Optimal control theory, Robust control principles, Non-linear control systems |
| EE 221 | Power Electronics | Elective | 3 | Power semiconductor devices, DC-DC converters (buck, boost, buck-boost), AC-DC rectifiers and inverters, PWM techniques, Electric drives applications, Grid-connected converters |
| EE 231 | VLSI Design | Elective | 3 | CMOS logic and fabrication process, Design rules and layout, Digital cell library design, ASIC design flow, Timing analysis and optimization, FPGA architectures and programming |
| EE 240 | Wireless Communication | Elective | 3 | Wireless channel modeling, Modulation and demodulation techniques, MIMO systems and spatial multiplexing, OFDM and multi-carrier communication, Cellular system principles, Cognitive radio concepts |
| EE 301 | Research Methodology | Core | 1 | Formulating research problems, Literature review and synthesis, Data collection and analysis methods, Scientific writing and presentation, Research ethics and integrity, Intellectual property rights overview |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| EE 203 | Signal Detection and Estimation | Core Elective | 3 | Statistical hypothesis testing, Bayesian estimation theory, Maximum Likelihood Estimation (MLE), Cramer-Rao Lower Bound, Kalman filtering, Non-linear estimation techniques |
| EE 210 | Digital Image Processing | Elective | 3 | Image enhancement and restoration, Image segmentation techniques, Feature extraction and representation, Image compression standards, Medical image analysis, Deep learning for images |
| EE 214 | Optimization for Machine Learning | Elective | 3 | Convex optimization fundamentals, Gradient descent and its variants, Stochastic optimization, Primal-dual methods, Regularization techniques, Large-scale optimization |
| EE 217 | Robust Control | Elective | 3 | Uncertainty modeling, Small gain theorem, H-infinity control, Mu-synthesis and analysis, Linear matrix inequalities (LMIs), Robust stability and performance |
| EE 222 | Advanced Power Systems | Elective | 3 | Power flow analysis techniques, Economic dispatch and unit commitment, Transient stability analysis, Voltage stability and collapse, Power system protection schemes, Smart grids and microgrids |
| EE 233 | Analog VLSI Design | Elective | 3 | MOSFET models for analog design, Current mirrors and references, Operational amplifier design, Comparators and voltage references, Data converters (ADCs/DACs), Phase-Locked Loops (PLLs) |
| EE 236 | Semiconductor Devices and Technology | Elective | 3 | PN junction theory and characteristics, Bipolar Junction Transistors (BJTs), MOSFET operation and scaling, Device fabrication processes, Advanced semiconductor materials, Optoelectronic devices |
| EE 241 | Information Theory | Elective | 3 | Entropy and mutual information, Source coding (Huffman, Lempel-Ziv), Channel capacity (Shannon''''s theorems), Error-control coding (linear, cyclic codes), Rate-distortion theory, Network information theory |
| EE 258 | Quantum Computing | Elective | 3 | Quantum mechanics postulates, Qubits and quantum states, Quantum gates and circuits, Quantum algorithms (Shor, Grover), Quantum error correction, Physical implementations of quantum computers |
| EE 263 | Internet of Things | Elective | 3 | IoT architectures and paradigms, Sensing and actuation technologies, IoT communication protocols (LoRa, Zigbee, MQTT), Cloud platforms for IoT, Edge and fog computing for IoT, Security and privacy in IoT |
