The engineering research methods course highlights on the principle and developmental process for conducting effective research and documentation.

The MPhil thesis involves a candidate working closely under the guidance of an approved graduate supervisor or supervisors for one year to investigate an approved original project idea from a selected field of specialization, and writing a thesis. The investigation will conform to the principles of research and will include the relationship of the proposed idea to previous related work reported in literature, significance of the study, the merits, short-comings and potential applications.

The seminar II course emphasizes on the development of the skill of candidates to enable them reach the research frontier of the discipline. As part of the course, scholarly articles will be selected from recent publications or book chapters to develop a good understanding and provide sufficient background to enable the formulation of novel hypotheses and experimental testing.

The analysis of genomic data course examines the approach for the analysis and display of large scale biological data sets using various algorithms and machine learning techniques. Topics include clustering techniques for gene expression and protein data analysis, machine learning techniques, biological networks, joint learning from multiple data sources, visualization issues for large scale biological data sets.

The algorithm design course provides the basic concepts and principles to examine and design efficient algorithms for a variety of computational problems and applications. Topics include dynamic programming, methods of algorithm design and analysis including data structures, network flows, matching, and linear programming, ellipsoid algorithm, probabilistic algorithm techniques, approximation algorithms for NP problems, geometric algorithms, number theoretic algorithms, on-line computation, and parallel computing.

The mathematical neurobiology course provides insight to the elements of neurophysiology and neuroanatomy for the development of quantitative models of nerve cell and brain phenomena and to develop and analyze several different mathematical models in neurobiology.

The bioinformatics course provides computational methods and development of algorithms to address problems in molecular biology.

The algorithms for computational biology course provide the background knowledge useful for the design of algorithms for analysis of biological systems.

The theory of computations course provides the fundamental complexity theory and models useful for solving computational problems. Topics include basic computational theory, computational models including nondeterministic alternating and probabilistic machines, Boolean circuits, complexity classes related to models of computing including NP, polynomial hierarchy, BPP among others, complete problems, interactive proof systems and probabilistic proofs, randomized algorithms, structural complexity and complexity hierarchy.

The digital image processing course examines the properties of digital images and the method of processing.