Meet ApplyKite Twin, the application assistant that learns with you
Meet your Twin, it learns with you
Your Twin learns with you
Launch week codes:30% off annual Pro20% off monthly Pro
Ends in00h00m00s
Ends in00h00m00s
Technical University of Denmark
Just Landed
Posted Yesterday
PhD Scholarship in Machine Learning for Optimization of Cyber-Physical Systems Technical University of Denmark in Denmark
Degree Level
PhD
Field of study
Computer Science
Funding
This is a full-time, three-year PhD appointment with salary and appointment terms governed by the collective agreement with the Danish Confederation of Professional Associations. The posting does not state a specific salary amount or separate tuition coverage.
Deadline
Nov 24, 2026
Country
Denmark
University
Technical University of Denmark
Apply for this position
Keywords
Suggested positions
About this position
DTU Compute is offering a PhD scholarship focused on developing machine-learning methods for faster analysis and optimization of cyber-physical systems. The project will connect learned prediction models with optimization methods to address analytical calculations and simulations that are too expensive to evaluate repeatedly within practical computing budgets.
The PhD researcher will develop surrogate models for predicting outputs such as execution delays, energy use, and resource demands. Machine learning will then be used to guide configuration, scheduling, and resource-allocation decisions while reducing reliance on costly model evaluations. The work will also assess solution quality, computing time, uncertainty, and transferability to new systems, with proposed solutions checked against original models and constraints.
The research is situated in the Embedded Systems Engineering section at DTU Compute and will be conducted with Professor Paul Pop and Associate Professor Silviu Craciunas. Potential application areas include software-defined vehicles, datacenters, industrial automation, and energy systems. The environment brings together research in embedded, distributed, and cyber-physical systems, including real-time analysis, optimization, and communication.
The doctoral researcher will publish research, collaborate with project partners, and contribute to teaching and student supervision. DTU Compute provides an international research setting with a substantial PhD community, academic events, onboarding activities, and a PhD club.
Funding details
This is a full-time, three-year PhD appointment with salary and appointment terms governed by the collective agreement with the Danish Confederation of Professional Associations. The posting does not state a specific salary amount or separate tuition coverage.
What's required
Applicants must hold a two-year master's degree (120 ECTS) or an equivalent qualification at the same academic level. A BSc and MSc in machine learning or artificial intelligence are preferred; degrees in computer science, applied mathematics, electrical engineering, or related fields with substantial machine-learning or AI coursework and projects are also relevant. Candidates should have a strong foundation in machine learning, algorithms, probability, and linear algebra; experience implementing and evaluating models in Python; and good written and spoken English. Experience with optimization, reinforcement learning, or simulation is advantageous. Applicants may apply before completing their master's degree but cannot begin until it has been awarded. Assessment considers academic qualifications, machine-learning and programming experience, research potential, and fit with the topic.
How to apply
Submit the complete online application through the “Apply now” portal by 24 November 2026 at 23:59 Danish time. Upload one PDF in English containing a motivation letter, CV, grade transcripts and BSc/MSc diploma with the official grading-scale description, plus a technical-writing example and code sample or repository link with a short explanation of your contribution.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.