Doctor of Technology in Applied & Agentic AI from GGU

Build strong foundations in algorithms and computational thinking.

Learn data structures, complexity, search, and sorting.

Explore dynamic programming and graph algorithms.

Understand constraint satisfaction and symbolic reasoning.

Apply concepts to planning and sequential decision-making.

Connect classical algorithms to modern AI applications such as retrieval, recommendation, routing, constrained generation, and AI agent decision loops.

Build strong foundations in predictive machine learning.

Frame business and scientific problems for ML applications.

Prepare and validate reliable datasets.

Develop regression and classification models.

Apply feature engineering techniques.

Evaluate model generalisation and performance.

Understand data leakage, class imbalance, and calibration.

Diagnose bias–variance trade-offs and model errors.

Address uncertainty and bias for more reliable models.

Learn best practices for model handoff and production readiness.

Explore advanced AI representation and decision systems.

Apply clustering, dimensionality reduction, and anomaly detection.

Build and evaluate recommender systems and ranking models.

Learn time-series forecasting for temporal data.

Understand causal reasoning and probabilistic modelling.

Explore foundations of reinforcement learning and sequential decision-making.

Apply advanced AI techniques to complex real-world problems.

Build foundations for Generative AI, LLM engineering, and agentic AI systems.

Build foundations in neural networks and deep learning.

Learn backpropagation, optimisation, and regularisation.

Explore convolutional and sequence models.

Understand attention mechanisms and transformers.

Apply representation and multimodal learning.

Explore graph neural networks (GNNs).

Learn transfer learning and efficient inference.

Apply neural architectures to vision, language, temporal, recommendation, and connected-data problems.

Understand the architecture and evolution of foundation models and Generative AI.

Explore tokenisation, embeddings, and transformer internals.

Learn about pre-training, scaling laws, and Mixture-of-Experts (MoE) architectures.

Understand context windows and model capabilities.

Explore instruction tuning, fine-tuning, and PEFT.

Learn preference optimisation and model alignment.

Examine multimodal models and reasoning systems.

Understand test-time compute and model evaluation.

Focus on engineering and understanding model behaviour, beyond simply using hosted APIs.

Design reliable agentic and AI-native systems.

Explore agent harness design, planning, and tool use.

Implement structured outputs, memory, and reflection.

Learn workflow orchestration and controlled autonomy.

Understand human oversight and multi-agent collaboration.

Explore interoperability and non-deterministic testing.

Focus on maintainability and robust enterprise integration.

Apply retrieval and memory as agent reasoning patterns.

Build data infrastructure for ML, deep learning, foundation models, retrieval systems, and AI agents.

Design batch, distributed, and streaming data pipelines.

Develop feature, training data, retrieval, and RAG pipelines.

Explore knowledge graphs and memory pipelines.

Understand data quality, provenance, labelling, and synthetic data.

Learn approaches for contamination control, metadata, and data lineage.

Address privacy, governance, and scalable knowledge access.

Engineer the underlying infrastructure required for production-ready agentic AI applications.

Build and operate production-grade AI systems at scale.

Explore distributed training, GPUs, and accelerator systems.

Learn model serving and high-performance inference.

Understand containers and orchestration for AI workloads.

Apply MLOps and LLMOps practices.

Implement AI observability, reliability, and monitoring.

Address AI security, governance, and risk management.

Learn cost engineering and capacity planning.

Treat AI as critical infrastructure, not just a software feature.

Explore emerging AI technologies and specialised domains.

Explore emerging AI technologies and specialised domains.

Engage with topics that extend core AI concepts and reflect evolving industry and research trends.

Explore Sovereign AI, Edge AI/TinyML, Causal AI, Federated Learning, and Quantum AI.

Understand emerging AI regulation and governance frameworks.

Explore algorithmic auditing and responsible AI scaling.

Engage with a rotating set of topics that are regularly refreshed based on research, industry, and policy developments.

Transition from graduate-level learning to doctoral-level inquiry.

Develop doctoral reading, critical synthesis, and scholarly communication skills.

Engage with frontier AI research, including applied AI, Generative AI, agentic systems, and responsible AI governance.

Identify and justify a significant doctoral problem and knowledge/practice gap.

Develop a research contribution framework.

Design an evidence and evaluation strategy for doctoral research.

Address ethical, governance, security, and impact considerations.

Present and defend research ideas through a doctoral seminar.

Assess mastery of doctoral-level research methods and analysis.

Integrate knowledge and skills developed across the foundation and advanced curriculum.

Evaluate readiness to progress to the dissertation phase.

Complete the qualifying examination after the required coursework or MS in Applied and Agentic AI.

Develop foundations in doctoral-level research design and methodology.

Explore qualitative, quantitative, and mixed-methods research.

Learn literature synthesis and data analysis techniques.

Understand research ethics and responsible research practices.

Apply research methods to real-world AI, technology, and organisational challenges.

Evaluate research designs and select appropriate research methods.

Connect research evidence with practical technology implementation.

Prepare a rigorous and defensible dissertation proposal.

Build doctoral-level expertise in qualitative and quantitative analysis for AI research.

Develop skills to select, apply, and evaluate appropriate research methods.

Explore statistical inference, effect size, uncertainty quantification, and benchmark analysis.

Apply qualitative methods including thematic analysis, grounded theory, case studies, expert interviews, and discourse analysis.

Understand mixed-method research designs.

Explore AI-specific evaluation methods such as LLM-as-judge, human evaluation, automated metrics, and red-teaming.

Focus on methodological fit, analytical validity, and responsible interpretation of research findings.

Develop a formal Dissertation Topic Proposal.

Refine the research direction established in TECH 809.

Define the research problem, significance, and research gap.

Synthesise relevant academic literature.

Develop clear research questions or objectives.

Articulate the proposed research methodology.

Assess ethical, responsible AI, and governance considerations.

Establish a feasible and academically defensible foundation for the dissertation.

Expand the approved Dissertation Topic Proposal into a comprehensive Dissertation in Practice Proposal.

Develop the research problem, literature foundation, and theoretical framework.

Define the complete research methodology, data sources, and analytical approach.

Address ethical and AI governance considerations.

Establish the research’s expected contribution to professional practice.

Demonstrate feasibility, methodological rigor, defensibility, and practical impact.

Present a well-scoped research proposal capable of generating meaningful insights, interventions, or recommendations.

Successfully defending the proposal leads to doctoral candidacy.