Key Skills

Job Description

Introduction 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. Job Description 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. Job Requirements 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). Conditions of Employment 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. Unlimited access to the modern on‑campus TU/e Student Sports Center at an exceptionally affordable rate. 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. A Staff Immigration Team and a tax compensation scheme (the Expat Scheme) for international candidates. On our website you can discover even more information about our conditions of employment. Build on your career at TU/e! About Us We are a leading international university where scientific curiosity meets a hands-on mindset. We work in an open and collaborative way with high-tech industries to tackle complex societal challenges. Our responsible and respectful approach ensures impact — today and in the future. TU/e is home to over 13,000 students and more than 7,000 staff, forming a diverse and vibrant academic community. Discover what Light Up means to TU/e in our short company video. Our university is located in Brainport Eindhoven — a world‑leading tech region with more than 7,000 high‑tech companies and strong R&D activity. Known for breakthroughs in AI, photonics, semiconductors and advanced manufacturing, Brainport is a place where technology serves people and society. Learn more about the Brainport region here. The mission of the Department of Electrical Engineering is to acquire, share and transfer knowledge and understanding in the whole field of Electrical Engineering through education, research and valorization. We work towards a ‘Smart Sustainable Society’, a ‘Connected World’, and a healthy humanity (‘Care & Cure’). Activities share an application-oriented character, a high degree of complexity and a large synergy between multiple facets of the field. Research is carried out into the applications of electromagnetic phenomena in all forms of energy conversion, telecommunication and electrical signal processing. Existing and new electrical components and systems are analyzed, designed and built. The Electrical Engineering department takes its inspiration from contacts with high-tech industry in the direct surrounding region and beyond. The department is innovative and has international ambitions and partnerships. The result is a challenging and inspiring setting in which socially relevant issues are addressed. Information Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Dr. Giulia de Pasquale ([email protected]) or Prof. dr. ir. Roland Toth ([email protected]). Visit our website for more information about the application process. You can also contact [email protected]. Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video. Are you inspired and would like to know more about working at TU/e? Please visit our career page. Application We invite you to submit a complete application by using the apply button. The application should include a: Cover letter in which you describe your motivation and qualifications for the position. Curriculum vitae, including a list of your publications and the contact information of three references. Kindly note that we may reach out to references at any stage of the recruitment process. We recommend notifying your references upon submitting your application. Ensure that you submit all the requested application documents. We give priority to complete applications. We look forward to receiving your application and will screen it as soon as possible. Please note You can apply online. We will not process applications sent by email and/or post. A pre-employment screening (e.g. knowledge security check) can be part of the selection procedure. For more information on the knowledge security check, please consult the National Knowledge Security Guidelines. Please do not contact us for unsolicited services.

Core Responsibilities

Develop safe Bayesian optimization and distributed learning algorithms for time-varying, high-dimensional, safety-critical systems, and establish rigorous convergence, regret, and safety guarantees. Validate the resulting theory through UAV tracking and obstacle-avoidance experiments and temperature control in a residential building, and publish and present the research.

Requirements

Applicants must have a master's degree or equivalent in Control Engineering, Electrical Engineering, Mechanical Engineering, or Mathematics; current master's students should state their expected graduation date. The role calls for a curious, research-oriented attitude, motivation to develop teaching and student-coaching skills, programming knowledge in Python or Julia, and fluent spoken and written English at C1 level.

Benefits

• Pension Scheme
• Paid Pregnancy And Maternity Leave
• Partially Paid Parental Leave
• Holiday Allowance
• Year-End Bonus
• Generous Annual Leave
• Training And Professional Development
• On-Campus Childcare
• Access To Sports Facilities
• Mental Health Support
• Commuting Allowance
• Work-From-Home Allowance
• Internet Cost Allowance
• Tax Compensation Scheme