An applied Swiss-inspired curriculum combining algorithmic rigour, systems architecture, digital trust and practical engineering capability for modern technology leaders.
The Executive Doctorate in Artificial Intelligence Leadership at Brookfield School of Computing is engineered to provide deep competency across core computer science, systems design, and targeted domain applications. Students develop fluency in software craftsmanship, distributed architectures, algorithm optimization, and data governance.
Instruction balances rigorous theoretical foundations with practical code repositories, laboratory simulations, and ethical technology review aligned with modern international computing standards.
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC101 | Philosophy of Technical Sciences & Epistemology in Artificial Intelligence Leadership | Theory | Examine computational ontology, paradigm shifts, falsificationism and scientific epistemology in Artificial Intelligence Leadership. |
| DOC102 | Doctoral Research Methodology & Design Science Research in Artificial Intelligence Leadership | Theory | Formulate Hevner's Design Science Research guidelines, rigor vs relevance cycles and doctoral artifact design in Artificial Intelligence Leadership. |
| DOC103 | Empirical Research Formulation & Quantitative Inquiry in Artificial Intelligence Leadership | Practical | Design formal empirical investigations, controlled computational experiments and statistical hypothesis test suites in Artificial Intelligence Leadership. |
| DOC104 | Theoretical Frameworks & Advanced Systems Modeling in Artificial Intelligence Leadership | Theory | Deconstruct axiomatic systems models, state transition systems and formal verification in Artificial Intelligence Leadership. |
| DOC105 | Computational Experimentation & Statistical Verification in Artificial Intelligence Leadership | Practical | Execute Monte Carlo simulations, power calculations and non-parametric statistical tests in Artificial Intelligence Leadership using R and Python. |
| DOC106 | Critical Literature Synthesis & State-of-the-Art Analysis in Artificial Intelligence Leadership | Theory | Perform systematic literature reviews (PRISMA), meta-analyses and theoretical gap identification in Artificial Intelligence Leadership. |
| DOC107 | Systems Epistemology & Architectural Thinking in Artificial Intelligence Leadership | Theory | Evaluate socio-technical systems theory, emergence in complex adaptive architectures and structural decoupling in Artificial Intelligence Leadership. |
| DOC108 | Research Ethics, Responsible Innovation & Swiss Standards in Artificial Intelligence Leadership | Practical | Audit doctoral research protocols against Swiss human research ethics, algorithmic safety and data governance in Artificial Intelligence Leadership. |
| DOC109 | Doctoral Research Scoping & Initial Proposal Portfolio in Artificial Intelligence Leadership | Theory | Author the preliminary doctoral scoping portfolio defining novel research questions, significance and scope in Artificial Intelligence Leadership. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC201 | Advanced Doctoral Proposal Defense & Technical Roadmap in Artificial Intelligence Leadership | Theory | Defend the comprehensive doctoral research proposal, methodology and experimental roadmap in Artificial Intelligence Leadership before a faculty panel. |
| DOC202 | Empirical Prototyping & Research Artifact Engineering in Artificial Intelligence Leadership | Practical | Construct the foundational computational artifact, reference architecture and experimental software testbed in Artificial Intelligence Leadership. |
| DOC203 | Advanced Statistical Learning & Model Evaluation in Artificial Intelligence Leadership | Theory | Derive generalization bounds, cross-validation asymptotics and high-dimensional estimation theory in Artificial Intelligence Leadership. |
| DOC204 | Field Experimentation & Data Collection Methodology in Artificial Intelligence Leadership | Practical | Deploy telemetry collectors, empirical logging probes and instrumentation across production testbeds in Artificial Intelligence Leadership. |
| DOC205 | Enterprise Systems Integration & Sociotechnical Impact Analysis in Artificial Intelligence Leadership | Theory | Analyze organizational adoption dynamics, human-in-the-loop interfaces and sociotechnical integration barriers in Artificial Intelligence Leadership. |
| DOC206 | Intellectual Property, Open Research & Technical Dissemination in Artificial Intelligence Leadership | Practical | Manage doctoral software patent disclosures, dual-licensing models and open-source scientific repository governance in Artificial Intelligence Leadership. |
| DOC207 | Technology Innovation Strategy & Industrial Research Collaboration in Artificial Intelligence Leadership | Theory | Structure university-industry research partnerships, industrial consortium agreements and technology transfer in Artificial Intelligence Leadership. |
| DOC208 | Mid-Candidature Doctoral Milestone & Progress Review in Artificial Intelligence Leadership | Theory | Successfully pass the comprehensive mid-candidature doctoral review demonstrating substantial empirical progress in Artificial Intelligence Leadership. |
| DOC209 | Applied Research Colloquium & Working Paper Series in Artificial Intelligence Leadership | Practical | Present working research papers, collect expert peer critique and author academic pre-prints in Artificial Intelligence Leadership. |
| Code | Module Title | Type | Discipline & Learning Outcome |
|---|---|---|---|
| DOC301 | Doctoral Fieldwork & Experimental Artifact Evaluation in Artificial Intelligence Leadership | Practical | Conduct exhaustive empirical fieldwork and evaluate the research artifact across multi-organization deployments in Artificial Intelligence Leadership. |
| DOC302 | Comprehensive Data Analysis & Computational Verification in Artificial Intelligence Leadership | Practical | Perform advanced statistical triangulation, sensitivity analysis and algorithmic verification of empirical data in Artificial Intelligence Leadership. |
| DOC303 | Doctoral Dissertation Drafting: Architectural & Empirical Synthesis in Artificial Intelligence Leadership | Theory | Draft dissertation core chapters: theoretical foundation, artifact architecture and empirical findings in Artificial Intelligence Leadership. |
| DOC304 | Doctoral Dissertation Drafting: Results Validation & Benchmarking in Artificial Intelligence Leadership | Practical | Author dissertation comparative benchmark chapters validating novel contributions against state-of-the-art baselines in Artificial Intelligence Leadership. |
| DOC305 | Academic Journal Manuscript & Peer Review Preparation in Artificial Intelligence Leadership | Theory | Prepare high-impact peer-reviewed journal submissions conforming to top-tier ACM/IEEE editorial standards in Artificial Intelligence Leadership. |
| DOC306 | Industry Technology Transfer & Executive Briefing in Artificial Intelligence Leadership | Practical | Synthesize academic findings into actionable executive advisories and commercial technology transfer briefs in Artificial Intelligence Leadership. |
| DOC307 | Final Doctoral Dissertation Submission & Quality Audit in Artificial Intelligence Leadership | Practical | Complete the full academic dissertation manuscript and pass formal institutional originality audits in Artificial Intelligence Leadership. |
| DOC308 | Doctoral Oral Defense (Viva Voce) Preparation in Artificial Intelligence Leadership | Theory | Master academic rhetorical defense, anticipate committee objections and articulate unique scholarly contributions in Artificial Intelligence Leadership. |
| DOC309 | Final Executive Doctoral Defense & Research Dissemination in Artificial Intelligence Leadership | Practical | Successfully defend the doctoral dissertation in public viva voce examination and disseminate scholarly findings in Artificial Intelligence Leadership. |
Master’s degree in computing or related quantitative discipline with significant technology leadership experience. Research concept proposal required.
Brookfield School of Computing is an independent private education institution in formation. Academic programmes, entry requirements, fees and awards are subject to institutional review and applicable Swiss legal regulations.