PhD in Efficient Online Optimization

Eindhoven University of Technology (TU/e); Nederland; On-site; EUR 36,000 - 45,000; Full time

Benefits offered by this job

ABP pension

Eight percent holiday allowance

Eight point three percent year-end-bon

Job summary

The Eindhoven University of Technology (TU/e) invites applications for a PhD candidate to develop safe Bayesian optimization for time-varying settings and distributed learning within the Control Systems group. The project focuses on theory with practical validation on drones and building climate control, under collaboration with Swiss partners.

You will contribute to algorithms, guarantees, and experiments, publish in leading venues, and participate in teaching tasks during four years.

Qualifications

• Master’s degree (or equivalent) in Control Engineering, Electrical Engineering, Mechanical Engineering, or Mathematics.
• If you are completing your MSc, indicate your expected graduation date in the cover letter.
• Research-oriented attitude with curiosity.
• Willingness to develop teaching skills and coach students.
• Programming knowledge (Python, Julia)?
• Fluent in spoken and written English (C1 level).

Responsibilities

• Develop safe Bayesian optimization algorithms for time-varying settings.
• Design distributed learning and optimization schemes with partial information and no central coordination.
• Study surrogate models and optimization to stay tractable in high dimensions.
• Derive convergence, regret and safety guarantees for non-stationary environments.
• Turn theory into algorithms validated on UAV reference tracking and building temperature control.

Skills

Python

Julia

English (C1)

Education

MSc in Control Engineering / Electrical Engineering / Mechanical Engineering / Mathematics

Job description

Eindhoven University of Technology (TU/e)

Organisation/Company Eindhoven University of Technology (TU/e) Research Field Engineering » Control engineering Researcher Profile First Stage Researcher (R1) Application Deadline 4 Dec 2026 - 22:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

How do you optimize a system you can’t fully model, where the operational objectives vary over time, and where safety is a major concern? Drones flying in cluttered changing environment, buildings that manage their own climate, and supply chains under changing market conditions and global events all face this problem. We are looking for a curious, mathematically minded PhD candidate to help build the theory that lets such systems learn on the fly, safely and efficiently and without a central coordinator. If you like elegant algorithms, mathematical challenges and seeing your theory tested on real drones and a real building, we’d like to hear from you.

Information

Bayesian optimization is a data-efficient way to optimize controllers and systems without explicit models, because it learns surrogate models and comes with built-in uncertainty estimates. Its theory still has major gaps when the system is large-scale, distributed and safety-critical, and when the reward and safety constraints change over time. In this project you will develop a new framework that combines safe Bayesian optimization, online learning and distributed estimation, with a strong focus on provable guarantees.

This is primarily theoretical research. Your main contributions will be new algorithms and convergence and safety guarantees. Experimental validation will be done together with our partners in the drone lab of the Control Systems group at TU Eindhoven and at the NEST building in Dübendorf, Switzerland.

You will be embedded in the Control Systems group at TU/e, contributing to its research program on intelligent and responsible control of networked systems.

As a PhD candidate, you will:

• Develop safe Bayesian optimization algorithms for time-varying settings, including adaptive sampling strategies that track moving optima while keeping constraints satisfied at every step.
• Design distributed learning and optimization schemes in which multiple agents, each with only partial information, cooperate without central coordination.
• Study how surrogate models and the optimization process can be broken down in time and space to stay tractable in high dimensions and over long horizons.
• Derive rigorous convergence, regret and safety guarantees for constrained large-scale optimization in non-stationary, safety-critical environments.
• Use tools from online learning, Gaussian processes, distributed estimation and control theory, and connect them in new ways.
• Turn your theory into algorithms that can be validated on UAV reference tracking and obstacle avoidance, and on temperature control in a real residential building in Dübendorf, Switzerland.
• Publish in leading venues in control, optimization and machine learning (e.g., CDC, TAC, NeurIPS, ICML), present your work internationally and work with our academic partners.

This PhD position is in collaboration with Dr. Marta Zagorowska from TU Delft and other collaboration partners in Switzerland.

• A master’s degree (or an equivalent university degree) in Control Engineering, Electrical Engineering, Mechanical Engineering, or Mathematics.
• In case you are still doing your MSc in the field listed above, please indicate your expected graduation date in the cover letter.
• A research-oriented attitude with curiosity
• Motivated to develop your teaching skills and coach students.
• Programming knowledge (Python, Julia)?
• Fluent in spoken and written English (C1 level).

Additional Information

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station.

In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities , scale P (min. € 3,204 - max. € 4,051 gross base salary per month (full-time)).
• In addition to your base salary, you will receive an 8% holiday allowance and an 8.3% year-end bonus, both calculated based on your annual gross base salary.
• Generous leave options: a standard 29 days (based on a 38 hour working week) per year that you can increase to 41 days by working two hours more per week (flexible working hours). This is prorated if you work part-time.
• As a TU/e employee, you participate in the ABP pension scheme, providing retirement pension and pension benefits for surviving dependents and occupational disability. TU/e pays 70% of the pension premium, while employees contribute the remaining 30%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, and on-campus children's day care.
• We support your wellbeing with free 24/7 access to OpenUp, providing you and your family with mental health support, expert guidance, and online training.
• An allowance for commuting, working from home and internet costs.

Additional comments

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr. Giulia de Pasquale ( [e-mail] ) or Prof. dr. ir. Roland Toth ( [e-mail] ).

Visit our website for more information about the application process . You can also contact [e-mail] .

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