IIIT Dharwad PhD Admission 2024 (Jan): Dates, Application Form

IIIT Dharwad PhD Admission
IIIT Dharwad PhD Admission

IIIT Dharwad PhD Admission 2024 (Jan): IIIT Dharwad, Karnataka invites online application for PhD admission for the academic session 2024 (Jan).

Check full details on IIIT Dharwad PhD Admission 2024 (Jan) here.

IIIT Dharwad PhD Programs

PhD Admission Categories

  • Regular (Full-time) – Institute fellowship or self-financed
  • Sponsored (Full-time or Part-time)
  • Part-time

PhD Programs

  • PhD Computer Science and Engineering
  • PhD Data Science and Intelligent Systems
  • PhD Electronics and Communication Engineering
  • PhD Physics
  • PhD Mathematics
  • PhD Humanities

Areas of Research

Areas of Research interests include Computer Science and Engineering, Data Science and Intelligent Systems, Electronics and Communication Engineering, Physics, Mathematics, and Humanities encompassing the following areas, but not limited to: Block Chain, Cloud Computing and Security, Coding Theory, Computer Networks, Computer Vision and Image Processing, Natural Language Processing, Parallel Computing, Speech Processing, Data Analytics, Internet of Things, Machine Learning, Deep Learning, Optimization, Signal Processing, Soft Computing, Social Networks, Physics, Quantum Computing, Mathematics and Statistics.

IIIT Dharwad PhD Admission 2024 (Jan) Eligibility Criteria

Ph.D. in Computer Science and Engineering / Data Science and Intelligent Systems / Electronics and Communication Engineering

To be eligible for admission, one of the following is a must

  • Master’s Degree in Engineering / Technology in relevant areas
  • B.E. / B.Tech / M.Sc. in Engineering / Technology or an equivalent degree, in relevant areas
  • B.E. / B.Tech. applicants with experience in a reputed R&D organization or a company, or those who have published papers in reputed conferences and journals, or patents are preferred.

PhD in Physics / Mathematics / Humanities

  • Master’s Degree in relevant areas is a must.
  • Minimum CGPA / CPI of 6.0 for General / OBC and 5.5 for S.C / S.T / PwD category students on a scale of 1-10 or 60% for General / OBC and 55% for S.C / S.T / PwD category students marks in aggregate.

IIIT Dharwad PhD Admission 2024 (Jan) Important Dates

EventDates
Date of notification22 Nov 2023
Last date for application forms08 Dec 2023

IIIT Dharwad PhD Admission 2024 (Jan) Application Process

  • Candidates have to apply online on the link available at IIIT Dharwad website.
  • Read instruction given there carefully before filling application form.
  • A separate application form has to be filled if the applicants are applying in more than one discipline and more than one scheme.
  • Applicants waiting for their qualifying exam results can also apply.
  • Reservation as per the Government of India rules is applicable.
  • Applicants should complete the application form in all respects. An incomplete application will not be considered.
  • Applicants must also provide the details of at least two well-acquainted people who can provide reference letters in support of the candidature of the applicant. At least one of them must be an academician and must have taught the applicant.

Selection Process

  • Candidates will be selected on the basis of screening test & interview.
  • There will be a screening test for all the shortlisted candidates. Questions will be of short answer type.
  • The selection process consists of a screening test followed by an interview. The candidates who qualify in the screening test will be allowed to appear for the interview. The fundamental knowledge of the candidates will be tested in their respective disciplines.

Syllabus

Computer Science and Engineering

  • Programming in C. Recursion. Arrays, stacks, queues, linked lists, trees, binary search trees, binary heaps, graphs. Searching, sorting, hashing. Asymptotic worst case time and space complexity.
  • Algorithm design techniques: greedy, dynamic programming and divide‐and‐conquer. Graph traversals, minimum spanning trees, shortest paths. Regular expressions and finite automata. Context-free grammars and push-down automata. Regular and context-free languages, pumping lemma.
  • System calls, processes, threads, inter‐process communication, concurrency and synchronization. Deadlock. CPU and I/O scheduling. Memory management and virtual memory. File systems. ER‐model. Relational model: relational algebra, tuple calculus, SQL. Integrity constraints, normal forms. File organization, indexing (e.g., B and B+ trees). Transactions and concurrency control.
  • Concept of layering: OSI and TCP/IP Protocol Stacks; Basics of packet, circuit and virtual circuit-switching; Data link layer: framing, error detection, Medium Access Control, Ethernet bridging; Routing protocols: shortest path, flooding, distance vector and link state routing; Fragmentation and IP addressing, IPv4, CIDR notation, Basics of IP support protocols (ARP, DHCP, ICMP), Network Address Translation (NAT); Transport layer: flow control and congestion control, UDP, TCP, sockets; Application layer protocols: DNS, SMTP, HTTP, FTP, Email. Data Science and Intelligent Systems
  • Probability and Statistics: Independent events, mutually exclusive events, conditional and joint probability, Bayes Theorem, and variance, mean, median, mode and standard deviation, correlation, and covariance, random variables.
  • Linear Algebra: Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, eigenvalues and eigenvectors, determinant.
  • Programming, Data Structures and Algorithms: Programming in Python, basic data structures: stacks, queues, linked lists, trees, hash tables; Search algorithms: linear search and binary search, basic sorting algorithms: selection sort, bubble sort and insertion sort; divide and conquer: mergesort, quicksort; introduction to graph theory; basic graph algorithms: traversals and shortest path.
  • Database Management: ER-model, relational model: relational algebra, tuple calculus, SQL, integrity constraints, normal form, file organization, indexing, data types, data transformation such as normalization, discretization, sampling, compression;
  • Machine Learning: (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross validation, multi-layer perceptron, feed-forward neural network; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, dimensionality reduction, principal component analysis.

Electronics and Communication Engineering

  • Network Analysis: Node and Mesh Analysis, Superposition, Thevenin’s Theorem, Norton’s Theorem, Time and Frequency Domain Analysis of Linear Circuits (RL, RC, RLC)
  • Signals and Systems: Continuous-Time Signals: Fourier Series and Fourier Transform, Linear Time-Invariant Systems: Properties, Causality, Stability, Convolution, Frequency Response
  • Analog Circuits: Diode Circuits: Clipping, Clamping, Rectifiers, BJT and MOSFET Amplifiers: Biasing, Small Signal Analysis, Op-Amp Circuits: Amplifiers, Differentiators, Integrators, Active Filters, Oscillators.
  • Digital Design and Microprocessors: Number Representations: Binary, Integer, Floating-Point Numbers, Combinatorial Circuits: Boolean Algebra, Logic Gates, Sequential Circuits: Latches, Flip-Flops, Counters, Data Converters: Sample and Hold Circuits, ADCs, DACs, Machine Instructions and Addressing Modes, Arithmetic Logic Unit (ALU), Data Path, Control Unit, Instruction Pipelining.
  • Control Systems: Feedback Principle, Transfer Function, Block Diagram Representation, Signal Flow Graph.
  • Communication Systems: Digital Modulation Schemes: ASK, PSK, FSK, QAM, Bandwidth and Communication Systems.
  • Analog Circuits: Diode Circuits: Clipping, Clamping, Rectifiers, BJT and MOSFET Amplifiers: Biasing, Small Signal Analysis, Op-Amp Circuits: Amplifiers, Differentiators, Integrators, Active Filters, Oscillators.
  • Digital Design and Microprocessors: Number Representations: Binary, Integer, Floating-Point Numbers, Combinatorial Circuits: Boolean Algebra, Logic Gates, Sequential Circuits: Latches, Flip-Flops, Counters, Data Converters: Sample and Hold Circuits, ADCs, DACs, Machine Instructions and Addressing Modes, Arithmetic Logic Unit (ALU), Data Path, Control Unit, Instruction Pipelining.
  • Control Systems: Feedback Principle, Transfer Function, Block Diagram Representation, Signal Flow Graph
  • Communication Systems: Digital Modulation Schemes: ASK, PSK, FSK, QAM, Bandwidth and Communication Systems.

Mathematics

  • Calculus : Functions of two or more variables, continuity, directional derivatives, partial derivatives, total derivative, maxima and minima, saddle point, method of Lagrange’s multipliers; Double and Triple integrals and their applications to area, volume and surface area; Vector Calculus: gradient, divergence and curl, Line integrals and Surface integrals, Green’s theorem, Stokes’ theorem, and Gauss divergence theorem. Finite dimensional vector spaces over real or complex fields;
  • Linear Algebra: Linear transformations and their matrix representations, rank and nullity; systems of linear equations, characteristic polynomial, eigenvalues and eigenvectors, diagonalization, minimal polynomial, Cayley-Hamilton Theorem, Finite dimensional inner product spaces, Gram-Schmidt orthonormalization process, symmetric, skew-symmetric, Hermitian, skew-Hermitian, normal, orthogonal and unitary matrices; diagonalization by a unitary matrix, Jordan canonical form; bilinear and quadratic forms.
  • Real Analysis: Metric spaces, connectedness, compactness, completeness; Sequences and series of functions, uniform convergence, Ascoli-Arzela theorem; Weierstrass approximation theorem; contraction mapping principle, Power series; Differentiation of functions of several variables, Inverse and Implicit function theorems; Lebesgue measure on the real line, measurable functions; Lebesgue integral, Fatou’s lemma, monotone convergence theorem, dominated convergence theorem.
  • Differential Equations:: First order ordinary differential equations, existence and uniqueness theorems for initial value problems, linear ordinary differential equations of higher order with constant coefficients; Second order linear ordinary differential equations with variable coefficients; Cauchy-Euler equation, method of Laplace transforms for solving ordinary differential equations, series solutions (power series, Frobenius method); Legendre and Bessel functions and their orthogonal properties; Systems of linear first order ordinary differential equations, Sturm’s oscillation and separation theorems, Sturm-Liouville eigenvalue problems, Planar autonomous systems of ordinary differential equations: Stability of stationary points for linear systems with constant coefficients, Linearized stability, Lyapunov functions.

Physics

  • Mathematical Physics: Vector calculus; differential equations and solutions involving special functions; complex analysis.
  • Classical Mechanics: Lagrangian formulation; central force motion; rigid body dynamics; Hamiltonian and Hamilton’s equations of motion, Hamilton-Jacobi equation.
  • Electromagnetic Theory: Solutions of electrostatic and magnetostatic problems including boundary value problems; Maxwell’s equations; electromagnetic waves.
  • Quantum Mechanics: Postulates of quantum mechanics; uncertainty principle; Schrodinger equation; Dirac Bra-Ket notation; particle in a box; harmonic oscillator; two and three dimensional systems; hydrogen atom; angular momentum and spin.

IIIT Dharwad PhD Admission 2024 (Jan) Results

IIIT Dharwad PhD Admission 2024 (Jan) Results will be available as per schedule. Results will be updates here as it will be available.

Important Links

NotificationClick Here
Application FormClick Here
IIIT Dharwad WebsiteClick Here