PhD in Physics

Mission Statement of Programme

"To develop advanced knowledge, skills, and research expertise in physics, empowering scholars to conduct original and impactful research that contributes meaningfully to the scientific community."

Objective of the Programme

Equip scholars with a deep understanding of fundamental and applied physics through rigorous coursework and research training.
Foster the ability to conduct original, high-quality research that contributes to scientific advancements and addresses real-world challenges.
Enhance analytical and problem-solving skills to enable scholars to develop innovative solutions in physics and related disciplines.
Promote effective dissemination of research through publications, presentations, and collaborations with the global scientific community.

Courses offered in PhD programme

Course Code	Course Title	Course content
CT-647	Statistical Analysis	Statistical Inference & Hypothesis Testing: Confidence and significance level, Sample size determination, Point & interval estimates, Interval estimates and hypothesis for Population Mean, Population standard deviation, & Population proportion, Chi-square tests. Regression and Correlation: Properties of least square, Simple linear regression, Nonlinear regression, Multiple regression, Statistical inference for regression, Choice of a Regression model, Correlation, Multiple and partial correlation, Coefficient of determination, Multicollinearity, Adequacy of the model. Multivariate Statistics: Multivariate data and models, Multivariate Normal distribution, Principal Component Analysis, Factor Analysis, Canonical Correlation, Correspondence Analysis.
CT-652	Machine learning	Basic concepts of machine learning, supervised learning, logistic regression, perceptron, exponential family, generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines, model selection and feature selection, Ensemble methods: Bagging, boosting. Evaluating and debugging learning algorithms, bias variance tradeoffs, worst case learning, clustering, K-means, PCA, ICA, reinforcement learning.
CT-646	Research Methodologies	Introduction to Research: Nature, Scope and purpose, Developing the research question and establishing the framework, Research Process, Ethics and considerations Types of Research: Types of Data and sources of data collection, Aims of research and its classification, Place & Time dimension in research Research Methods: Scientific research designs, Experimental designs, Causal Comparative & Correlational Designs, Survey Research, Qualitative Research Designs, Historical Research Methods and Tools: Instrumentation, Sampling, Validity & Reliability
CT – 664	Quantum Computation and Information	Basic concepts: Postulates of Quantum Mechanics Quantum bits – qubits, Dirac notation, combining qubits using the tensor product Measuring qubits, Performing operations on qubits The Bloch Sphere representation The quantum circuit model Simple quantum protocols: teleportation, superdense coding. Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm Shor’s algorithm for factoring Grover’s algorithm for searching Entanglement and Bell’s theorem Open quantum systems Quantum error correction Quantum cryptography • Emerging research topics from quantum computation and information