This document outlines the course requirements for a doctoral education programme. It lists required courses in four main categories: research skills learned on the job, research skills doctoral education courses, transferable skills mandatory and regular courses, and discipline-related skills mandatory and advanced courses. Research skills are developed through experiences like writing proposals and papers. Transferable skills courses cover topics such as scientific integrity and time management. Discipline courses focus on subjects specific to the student's field, like advanced algorithms, artificial intelligence, and discrete optimization.

1. Doctoral Education Programme
Course Guide
September 2021

3. 9/7/2021 3

4. Research Skills – Learning on the Job Activities
 Writing a research proposal (2 credits)
 Paper review (1 credit)
 Writing the first conference paper (1 credit)
 Writing the first journal article (2 credits)
 Supervising a MSc student/ Bachelor project groups (2-6 credits)
 Teaching assistance: designing examination assignments (1 credit)
 Teaching assistance: designing laboratory test (2-3 credits)
 Addressing a major international audience (1 credit)
 Addressing a small audience (0.5 credit)
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5. Research Skills – DE Courses
 Speedreading and Mindmapping (1,5 credits)
 Research Data Management 101 (1,5 credits)
 Basic Problem Solving & Decision-making for researchers (1 credit)
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DE course LOJ

6. Transferable Skills- Mandatory Courses
 PhD Startup Module A - Online Onboarding Module A (0.5 crdt)
 PhD Startup Module B - Noble Day (1,5 crdts)
 PhD Startup Module C – Scientific Integrity (0.5 crdts)
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7. Transferable Skills – Regular DE Courses
 Assessing Students and Master Thesis Projects (1 credit)
 Foundations of Educational Design (1 credit)
 Data visualisations: A practical approach (1 credit)
 Creative Tools for Scientific Writing (3 credits)
 Advanced writing in English for the University -Placement test
needed (October) (2 courses- 2x2 = 4 credits)
 Time Management I – Foundation (1 credit)
 Brain Management (on-campus) (2 credits)
 Dutch for foreigners (3 credits)
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Mandatory DE course

8. Discipline-related skills – Mandatory Courses
 TPM Research Frontiers (5 credits)
 PhD Days (2 credits)
 TPM GS Seminars (2 credits)
 TPM Peer Group (a.m.g.zuiderwijk-vaneijk@tudelft.nl) (2 credits)
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9. Discipline-related skills – Advanced School for
Computing & Imaging (ASCI)
 A Programmer's Guide for Modern High-Performance Computing (3
credits)
 Springschool on heterogeneous Computing Systems (4 credits)
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Mandatory
ASCI (Advanced School for Computing & Imaging)
(Faculty Doctoral Courses)

10. Discipline-related skills – Faculty Courses .Vol1
 EPA1361- Model-based Decision-making
 SEN9110- Simulation Masterclass
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Mandatory
ASCI (Advanced School for Computing & Imaging)
(Faculty Doctoral Courses)

11. Discipline-related skills – Faculty Courses .Vol2
 Advanced Discrete Optimization (Prof. Karen Aardal)
 IN4301: Advanced Algorithms, IN4344
 IN4010(-12) Artificial Intelligence Techniques(Prof. Jonker /not
responsible)
 CSE3210, Collaborative Artificial Intelligence
 CS4305TU, Applied Machine Learning
 WI4410, Advanced Discrete Optimization (Prof. Karen)
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Mandatory
ASCI (Advanced School for Computing & Imaging)
(Faculty Doctoral Courses)

12. WI4410: Advanced Discrete
Optimization (Q3 / 6 credits)
• Integer optimization: Polyhedral theory; Cutting planes; Gomory
mixed-integer cuts, split cuts and their relation; Lift-and-project;
• IP in fixed dimension: Lattices and basis reduction;
• IP in fixed dimension: Lenstra's algorithm and extensions
resp: Prof.dr.ir. K.I. Aardal
• modeling and solving using linear programming
* exact algorithms using search trees, dynamic programming,
and/or decision diagram
• Resp: Dr. M.M. de Weerdt , Prof.dr.ir. K.I. Aardal
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IN4301: Advanced Algorithms,
IN4344 (Q1 / 5 credits)

13. IN4010(-12) Artificial
Intelligence Techniques (Q1)
- Students have a general overview of decision-
theoretic artificial intelligence techniques
- Students understand the working of the artificial
intelligence techniques discussed
- Students are able to design, implement and
evaluate algorithms for complex decision making
problems.
Resp: Dr. F.A. Oliehoek , Prof.dr. C.M. Jonker
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CSE3210, Collaborative
Artificial Intelligence (Q3, Bc)
• Co-active design of decentralized AI systems
Negotiation among autonomous agents
Agent communication and interaction protocols
Agent coordination mechanisms
• Resp: Dr. P.K. Murukannaiah

14. CS4305TU, Applied
Machine Learning
• Explain how different
machine-learning
techniques can be used for
different types of problems
• Identify the advantages and
disadvantages (including
ethical and societal
concerns) of using different
machine-learning
techniques on different
types of data
• Apply machine-learning
techniques to real-world
problems
Resp: M.L. Tielman
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15. Thank you!