DRAT Syllabus

The Digital University Research Aptitude Test (DRAT) is the entrance test for PhD admissions. It consists of a common aptitude test followed by a subject-specific test and interview.

Digital University Research Aptitude Test (DRAT)

Students who have secured 50 % marks in the DRAT are eligible to be called for the interview. A relaxation of 5 % marks will be allowed in the entrance examination for the candidates belonging to SC/ST/OBC/differently-abled category, Economically Weaker Section (EWS). DRAT comprises three stages:

DRAT-Common (DRAT-C)

DRAT-C is an aptitude test common to all PhD applicants. It is conducted as an AI- and human-proctored online examination that candidates can take from home.

DRAT-C is a multiple-choice (MCQ)-based examination of 35 marks that evaluates research aptitude along with supporting analytical and quantitative abilities. It eIt comprises four components: English Comprehension (5 marks), Quantitative Aptitude (10 marks), Research Aptitude (10 marks), and Analytical Aptitude (10 marks). The English Comprehension component assesses the ability to comprehend and interpret research literature, as well as proficiency in academic vocabulary, grammar, and research communication, including questions on reading comprehension, vocabulary, grammar, sentence correction, synonyms, and antonyms. The Quantitative Aptitude component evaluates numerical reasoning and quantitative analysis skills essential for research, including data interpretation, basic statistical understanding, and mathematical problem-solving. The topics include number systems, percentages, profit and loss, ratios, averages, time and work, algebra, and data interpretation. The Research Aptitude section covers research fundamentals, research types, research design, hypothesis testing, sampling methods, data collection, referencing, and research ethics. Analytical Aptitude tests logical reasoning skills through pattern recognition, series and sequences, syllogisms, analogies, data sufficiency, and critical reasoning. Total: 35 marks

DRAT-Subject specific (DRAT-S)

The DRAT-S will be based on the specific research area applied for, under the respective schools or recognised research centres of Digital University Kerala. Candidates who qualify the DRAT-Common (DRAT-C) with a minimum of 50% will proceed to the DRAT-S (subject-specific) examination, followed by an interview. DRAT-S and an interview will be conducted at Digital University Kerala, Thiruvananthapuram.

The detailed syllabus for DRAT-S is given in annexure I. (Total: 35 Marks)

Interview

The interview, carrying a total of 30 marks, evaluates the candidate’s subject knowledge and readiness for independent investigation. Applicants may present their research interests and discuss them with faculty to evaluate alignment with ongoing research areas.

Annexure I

School of Computer Science and Engineering (SoCSE)

Test Code: SoCSE_DRAT01

Research areas: Machine Learning, Deep Learning Syllabus: Computer science fundamentals covering mathematical foundations of computing, such as linear algebra (vector space, inner product space, normed vector space, eigenvalues, eigenvectors, systems of linear equations and solutions, LU and singular value decomposition), probability and statistics (Bayes’ theorem, probability distributions, hypothesis testing), and optimisation techniques (gradient descent, constrained and unconstrained optimisation). Programming, data structures, and algorithms include Python programming, basic data structures, searching and sorting, and graph algorithms. Database management covers the ER model, the relational model, SQL, integrity constraints, indexing, data transformation, including normalisation, sampling, and compression. The machine learning section includes supervised learning (regression, classification, SVM, decision trees, random forests, ensemble methods), unsupervised learning (clustering, dimensionality reduction using PCA and LDA), and model evaluation metrics. Deep learning covers neural networks (perceptron, MLP, backpropagation), optimization, regularization, convolutional neural networks (CNN), recurrent neural networks (RNN), LSTM, GRU, transformers, and large language models (LLMs).

Test Code: SoCSE_DRAT02

Research areas: Computer Networks and Security

Syllabus

Computer science fundamentals covering mathematical foundations of computing such as linear algebra (vector space, matrices, inner product space, normed vector space, eigenvalues, eigenvectors, systems of linear equations and solutions, LU and singular value decomposition), probability and statistics (Bayes’ theorem, probability distributions, hypothesis testing), and optimisation techniques (gradient descent, constrained and unconstrained optimisation). Programming, data structures, and algorithms include Python programming, basic data structures, searching and sorting, and graph algorithms. Analysis of algorithms (algorithm efficiency, design techniques, computational complexity), computer organization and architecture (computer structure, instruction execution, memory hierarchy, I/O interface), theory of computation (automata, formal languages, Turing machines, computational complexity), operating systems (processes and threads, memory management, file systems, concurrency, system security), and computer networks and security (network protocols, addressing, routing, transport mechanisms, cryptography, security).

School of Digital Humanities, Library and Information Sciences (SoDHILA) Research areas: Technology Management, Entrepreneurship, Supply Chain, Human Resource Management, Organisational Behaviour

Test Code: SoDHILA_DRAT03

Syllabus

Management concepts and functions; organisational behaviour elements such as personality, perception, motivation, leadership, group dynamics, communication, organisational culture, change management, and stress management. HR planning, industrial relations, employee engagement, strategic HRM, HR analytics, business ethics and corporate governance, statistics for management, operations research, strategic management, entrepreneurship development, marketing management, operations management.

School of Digital Sciences (SoDS)

Test Code: SoDS_DRAT04

Research areas: 1. Computational Fluid Dynamics & Scientific Machine Learning; 2. Computational Nonlinear Dynamics, 3. Network of Oscillators & Neurodynamics; 4. Computational Neuroscience; 5. Scientific Computing, Machine Learning & Physics-Informed Neural Networks.

Syllabus

Linear Algebra: vector space, eigenvalues, trace, determinants, singular value decomposition. Calculus: Limits, Differentiation, Definite Integration, L'Hôpital's rule, discontinuity. Differential Equations: slope fields, separation of variables, existence and uniqueness of solutions. Numerical Methods: Root finding methods, Euler method for ODE, RK4 method for ODE's, Gradient Descent, Computer basics & programming

Test Code: SoDS_DRAT05

Research areas: 1. AI-Enabled Molecular Design & Computational Chemistry; 2. AI-Driven Approaches in Molecular Design, Synthesis & Properties

Syllabus

Organic Chemistry: Reaction Mechanisms, Name Reactions, Stereochemistry, Aromaticity, Pericyclic Reactions, Photochemistry, Retrosynthesis, Spectroscopy. Medicinal Chemistry and Drug Discovery: Drug Design and Development, Structure–Activity Relationship (SAR), Pharmacokinetics (ADME/T) and Pharmacodynamics, Target Identification, Mechanisms of Drug Action, Virtual Screening, Pharmacophore Modelling, ADMET Prediction. Computational Chemistry: Molecular Mechanics, Quantum Chemistry, Density Functional Theory (DFT), and Molecular Dynamics. Machine Learning and AI in Chemistry: Supervised and Unsupervised Learning, Regression, Classification, Clustering, Neural Networks and Deep Learning, QSAR/QSPR Modelling, Graph Convolutional Networks (GCN), Transfer Learning, Model Interpretability.

Test Code: SoDS_DRAT06

Research areas: 1. Geospatial Modelling & Prediction, Geospatial Analytics, Geo-AI; 2—Microwave Remote Sensing and AI for Earth Observation.

Syllabus

GIS and remote sensing fundamentals, spatial data models (vector and raster), geospatial data processing, spatial interpolation, and visualisation. probability and statistics for spatial data, spatial autocorrelation, spatial regression, and spatial point pattern analysis. Machine Learning and Deep Learning applications in geospatial sciences: spatio-temporal prediction, time series analysis, spatio-temporal data modelling, change detection, and big data analytics. land-use and land-cover change prediction, soil and crop monitoring, climate and hydrological modelling, urban growth, infrastructure planning, and disaster risk assessment.

School of Electronic Systems and Automation (SoESA)

Test Code: SoESA_DRAT07

Research areas: Quantum Image Processing, Synthetic Aperture Radar (SAR) Image Acquisition and Processing

Syllabus

Digital Signal Processing (DSP), Digital Image Processing (DIP), Fundamentals of Quantum Image Processing (QIP), Synthetic Aperture Radar (SAR) Image Acquisition and Processing

School of Informatics (SoI)

Test Code: SoI_DRAT08

Syllabus

Concepts of sustainability, environmental, social, and economic dimensions, global frameworks for sustainability, social-ecological systems and coupled human–natural systems, thresholds, climate change, tipping points, and regime shifts, environmental risk and impact assessment, environmental management, sustainable resource management- circular economy and sustainable consumption, nature-based solutions, climate adaptation and mitigation, net zero pathways, ecosystem restoration, Innovation for sustainable development, sustainability indicators and metrics, sustainability analytics

Test Code: SoI_DRAT09

Syllabus

Eco-physiology: Phenological studies, plant-environment interactions,phenological monitoring using both field observations and remote sensing techniques, floral radiometry, colour science

DUK Recognised Research Centre – CMET Thrissur

Test Code: CMET_DRAT10

Research areas: Sensors and Actuators, Graphene and 2D Materials, Energy Storage Technologies

Syllabus

Classification of materials including metals, ceramics, polymers, and composites; mechanical properties such as stress-strain response, elastic, anelastic, and plastic deformation at room temperature; electronic properties including free electron theory, Fermi energy, density of states, elements of band theory, semiconductors, Hall effect, dielectric, piezoelectric, and ferroelectric behavior; magnetic properties including origin of magnetism, paramagnetism, diamagnetism, ferromagnetism, ferrimagnetism; thermal properties such as specific heat, thermal conduction, thermal diffusivity, thermal expansion, thermoelectric effects; optical properties including refractive index, absorption, transmission of electromagnetic radiation, with examples of materials and their applications; and electronic devices including energy bands in semiconductors, carrier transport by diffusion and drift, mobility and resistivity, generation and recombination of carriers, Poisson and continuity equations, PN junctions, Zener diodes, BJTs, MOS capacitors, MOSFETs, LEDs, photodiodes, and solar cells.

DUK Recognised Research Centre - ICAR_CTCRI

Research area 1

Research areas: 1. Artificial Intelligence and IoT applications in Agriculture, Computer simulation of Agriculturally important systems and Agrobotics

Syllabus

Fundamentals of Artificial Intelligence (AI), Supervised, Unsupervised and Reinforcement Learning, Neural Networks, Deep Learning, Data Preprocessing, Data Visualization, Big Data Technologies, Python/PHP, NumPy, Pandas, Scikit-learn, TensorFlow, Keras, IoT Applications in Agriculture, Basics of Robotics, Sensor technology.

Research area 2

Research areas: 2. Technology commercialisation; Technology entrepreneurship and incubation: Intellectual Property Management

Syllabus

Social science research methods, including qualitative and quantitative research, survey research, sampling techniques, interview schedules, focus group discussions, scaling techniques, participatory methods, data collection, statistical analysis, interpretation, and report writing. Research ethics, plagiarism and research communication; Understanding of interdisciplinary research approaches, socio-economic assessment, impact evaluation, stakeholder analysis, policy analysis, and innovation diffusion studies. Intellectual Property Rights (IPR), including patents, copyrights, trademarks, industrial designs, geographical indications, plant variety protection, trade secrets, patent drafting, patent search, licensing, technology transfer, and IP management. Innovation systems, technology development, translational research, technology readiness levels (TRLs), proof of concept, product development, technology commercialisation pathways, entrepreneurship, startup ecosystem, business incubation, startup mentoring, venture capital, and innovation management. Science, technology and innovation (STI) policy, National IPR Policy, biotechnology and agricultural innovation policies, biodiversity, bioethics, biosafety, regulatory frameworks, open innovation, artificial intelligence, sustainability, circular bioeconomy, and emerging policy issues related to science, technology, and innovation.

Research area 3

Research areas: 3. Bioinformatics and Genomics

Syllabus

Module1: Introduction to bioinformatics: Applications of Bioinformatics in Various Areas, Overview of Available Bioinformatics Resources on the Web, Protein and Genome; Information Resources and Analysis Tools; Established Techniques and Methods; Sequence File Formats FASTA, GenBank, FASTQ and Structured File Formats,

Module 2: Biological Databases: Protein Sequence and Structural Databases, Nucleotide Sequence Databases; NCBI, PubMed, Protein Data Bank (PDB), PIR, UniProt, EMBL, GenBank, DDBJ, SRA, UniGene; Specialized Databases: Pfam, SCOP, GO, Metabolic Pathways.

Module 3: Comparative and Functional Genomics: Pairwise sequence alignment methods; Heuristic Methods; BLAST and its variants, Statistics of Sequence Alignment Score, Scoring Matrices and Gap Penalty; Multiple Sequence Alignments, Phylogenetic Analysis, Secondary Structure analysis, Molecular Phylogenetics, Gene Expression Analysis using Microarrays and RNA-Seq, Determining the Functions of Individual Genes, Pathway and GO annotation systems, Non-coding DNA, gene prediction methods and tools. Genome Evolution, Genome and Transcriptome Assembly, Artificial intelligence techniques in chemistry.

Module 4. NGS Data Analysis, Bioinformatics Skills & Software Required for NGS Data Analysis. Transcriptomics by RNA-Seq: Principle of RNA-Seq; Experimental Design, RNA-Seq Data Analysis, Identification of Differentially Expressed Genes, Functional Analysis of Identified Genes, Small RNA Sequencing Data analysis, Genotyping and Genomic Variation Discovery, Single Nucleotide Variant (SNV) and Indel Calling, Variant Call Format (VCF) File, Evaluating VCF Results.

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