Introduction
The Council of
Scientific and Industrial Research (CSIR) conducts the National
Eligibility Test (NET) for awarding the Junior Research Fellowship (JRF)
and Lectureship in various disciplines, including Mathematical Sciences.
This examination tests candidates on their expertise in core mathematical
concepts, including Analysis, Algebra, Complex Analysis, Differential
Equations, Numerical Analysis, and Probability Theory.
This blog provides a detailed
syllabus breakdown, ensuring that aspirants have a clear understanding of
the exam topics and structure.
The
CSIR-NET Mathematical Science exam consists of two key sections:
· Part B & Part C: Common syllabus covering fundamental and
advanced topics in Mathematics, Statistics, and Theoretical Computer Science.
· Subject-Specific Specialization: Candidates specializing in Mathematics
must answer questions from Units II and III, while those in Statistics
focus on Unit IV.
· Set Theory:
Finite, countable, and uncountable sets.
· Real Number System:
Ordered field, Archimedean property, supremum & infimum.
· Sequences and Series:
Convergence, limsup, liminf, and fundamental theorems.
· Continuity & Differentiability:
Mean value theorem, sequences and series of functions, uniform convergence.
· Integration:
Riemann sums, improper integrals, monotonic functions, and Lebesgue integral.
· Functions of Several Variables:
Directional derivatives, implicit function theorem.
· Metric Spaces:
Compactness, connectedness, and normed linear spaces.
· Vector Spaces:
Subspaces, basis, dimension, algebra of linear transformations.
· Matrices:
Rank, determinant, eigenvalues & eigenvectors, Cayley-Hamilton theorem.
· Canonical Forms:
Change of basis, Jordan form, triangular form.
· Inner Product Spaces:
Orthonormal basis, quadratic forms, and classification.
· Complex Numbers: Plane representation, power series,
transcendental functions.
· Analytic Functions: Cauchy-Riemann equations, contour
integrals.
· Fundamental Theorems: Cauchy’s theorem, Liouville’s theorem,
Schwarz lemma.
· Series and Residues: Taylor series, Laurent series, residue
calculus.
· Conformal Mappings: Möbius transformations and applications.
· Combinatorics: Permutations, combinations, pigeon-hole
principle.
· Number Theory: Fundamental theorem of arithmetic,
divisibility, Chinese remainder theorem.
· Group Theory: Normal subgroups, quotient groups, Sylow
theorems.
· Ring Theory: Ideals, unique factorization, Euclidean
domains.
· Field Theory: Finite fields, field extensions, and Galois
theory.
· Topology: Basis, dense sets, compactness,
connectedness.
· Existence and Uniqueness: Initial value
problems.
· First-order ODEs: Singular solutions,
general theory of homogeneous and non-homogeneous ODEs.
· Sturm-Liouville Problems: Green’s function,
boundary value problems.
· First Order PDEs: Lagrange and
Charpit methods.
· Second Order PDEs: Classification,
method of separation of variables (Laplace, Heat, Wave equations).
· Solving Equations: Iteration methods, Newton-Raphson method.
· Linear Algebraic Equations: Gauss elimination, Gauss-Seidel methods.
· Interpolation: Lagrange, Hermite, spline interpolation.
· Numerical ODEs: Euler, Runge-Kutta methods.
· Euler-Lagrange Equations: Necessary and sufficient conditions
for extrema.
· Variational Methods: Boundary value problems.
· Fredholm and Volterra Equations: Separable kernels,
resolvent kernel.
· Lagrangian and Hamiltonian Mechanics: Principle of least action.
· Rigid Body Motion: Euler’s equations, small
oscillations.
UNIT – 4: Probability, Statistics, and
Optimization
· Probability Space: Sample space, discrete probability, Bayes’
theorem.
· Random Variables: Expectation, moments, characteristic
functions.
· Limit Theorems: Weak & strong laws of large numbers,
Central Limit Theorem.
· Markov Chains: Classification of states, stationary
distribution.
· Distributions: Standard discrete & continuous distributions.
· Estimation & Hypothesis Testing: Confidence intervals, likelihood ratio
tests.
· Regression & ANOVA: Linear regression, logistic regression,
analysis of variance.
· Multivariate Analysis: Principal component analysis, discriminant
analysis.
· Sampling Techniques: Simple random sampling, stratified
sampling.
Optimization & Queueing Theory
· Linear Programming: Simplex
method, duality.
· Queueing Models: M/M/1, M/M/C,
inventory models.