PhD in Efficient Online Optimization

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 (g.de.pasquale@tue.nl) or Prof. dr. ir. Roland Toth (r.toth@tue.nl).

Visit our website for more information about the application process. You can also contact HRServices.ee@tue.nl.

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.

PhD in Efficient Online Optimization

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.

Robotics Technology Center Engineer

Introduction
Would you like to contribute to transferring TU/e robotics research into the industry? Are you able to translate scientific developments in robotics to compelling demonstrators showcasing the possibilities of the technology in actual industrial use-cases?

Job Description
You will develop and maintain high-quality demonstrators (TRL 4-6) of robotic systems that showcase the value of scientific developments in robotics (TRL 2-4). This includes implementing methods from state-of-the-art robotics research and translating them into stable, robust, and professional demonstrators that can be witnessed and used by/with industrial partners. Although part of your role will be based on 2- to 3-month, goal-oriented projects with industry, you are responsible for consolidating results/knowledge back into a common stack and/or infrastructure.

The Robotics Technology Center
The RTC aims to accelerate the adoption of advanced robotics by translating TU/e research into impactful demonstrators and prototypes that showcase opportunities, reduce innovation barriers, and create value for industry and society. Our approach is to develop pre-competitive demonstrators together with researchers and industry partners, validate innovations in realistic environments, and advance technologies along their pathway to societal and economic impact.

The RTC enables industry to validate advanced robotic technology through a test-before-invest approach: using state-of-the-art robotics research outputs, university infrastructure, and robotic platforms, potentially provided by industry. This results in a recommendation regarding task automation and a referral to the appropriate ecosystem partner for commercial follow-up..

Key Responsibilities
In this role, you will:

• Design, develop, and deploy robotic demonstrators based on TU/e research outcomes.
• Translate cutting-edge robotics research into compelling demonstrators that address real industrial challenges.
• Integrate and implement new software, algorithms, and control solutions into existing robotic platforms.
• Work with a broad range of robotic systems and laboratory infrastructure within the Mechanical Engineering department and industry platforms.
• Collaborate closely with researchers, engineers, and industry partners to define demonstrator requirements and use cases.
• Validate robotic solutions in realistic application environments and support technology maturation towards industrial adoption.
• Maintain and continuously improve demonstrators to ensure reliability, robustness, and professional presentation quality.
• Keep demonstrator software up to date and ensure compatibility with evolving research and platform developments.
• Support demonstrations, workshops, and industry visits by showcasing robotic technologies and explaining their capabilities and potential impact.

Job Requirements

• You hold an MSc or BEng in Mechanical Engineering, Mechatronics Engineering, or Embedded Systems.
• You have proven experience working with robotic systems, preferably in a commercial, open-world setting.
• You are a positive-minded team player who enjoys programming robotic systems and working with colleagues from scientific and business perspectives to build professional demonstrators.
• You have the ability to work both independently and as part of a multidisciplinary team, with a service-oriented mindset.
• You combine a curiosity to state-of-the-art robotic developments with a constructive and critical thinking.
• You are proficient in English, both orally and in writing.

If you are concerned that you don’t meet all the requirements sufficiently, but the job does excite you, then be sure to respond, we would love to look at your profile.

Conditions of Employment
An exciting position within an international yet personal university. You are right in the middle of the students, on a green campus within walking distance of the central station. Besides beautiful architecture, you will find varied workplaces and excellent sports facilities.

We also offer you:

• Salary in accordance with the Collective Labour Agreement for Dutch Universities, scale 11, ranging from €4,925 to €6,682 gross base salary per month (full-time), depending on your knowledge and experience.
• 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%.
• A favorable arrangement for more holidays or a sabbatical.
• A selection model for additional fringe benefits.
• Working hours in consultation for an optimal work-life balance.
• 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.
• Scope for your talent with advancement prospects and excellent development opportunities such as mentoring, workshops and coaching.
• Partially paid parental leave and reimbursement for commuting expenses, working from home and the internet.

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 Department of Mechanical Engineering department conducts world-class research aligned with the technological interests of the high-tech industry in the Netherlands, with a focus on the Brainport region. Our goal is to produce engineers who are both scientifically educated and application-driven by providing a balanced education and research program that combines fundamental and application aspects. We equip our graduates with practical and theoretical expertise, preparing them optimally for future challenges.

Information
Do you recognize yourself in this profile and would you like to have more information about the position, please contact the hiring manager Wouter Kuijpers, co-founder Robotics Technology Center, w.j.p.kuijpers@tue.nl or +31 40 247 4508. You can also contact Jordy Senden, co-founder Robotics Technology Center, j.p.f.senden@tue.nl.

Visit our website for more information about the application process. You can also contact HRServices.me@tue.nl.

Are you inspired and would like to know more about working at TU/e? Please visit our career page.

Application
If you are interested, please use the ‘apply’ button to send us your CV and letter of application with your motivation and a description of your qualifications. Ensure that you submit all the requested application documents. We give priority to complete applications.

Screening of candidates begins as soon as applications are received and continues until the position is filled. Where applicable, internal candidates will be given priority over external candidates where they are equally suitable.

We look forward to receiving your application!

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.
• Important for non-EU applicants: Please be aware that for this position, specific residence permit requirements apply. If you are a non-EU national, you may not be eligible to legally work in this role under current Dutch immigration regulations. We strongly advise you to contact our Staff Immigration Team (staffimmigration@tue.nl) before applying to check your eligibility and to receive further guidance.
• Please do not contact us for unsolicited services.

PhD in Fair Distributed Optimization and Control of Large-Scale Networked Systems

Introduction
Can thousands of autonomous agents, each with only local information, reach decisions that are optimal, stable and provably fair? In this PhD you will build the distributed optimization and control theory that answers this question. You will design scalable multi-agent algorithms with fairness built in, handle uncertainty with stochastic and robust methods, and prove convergence, stability and fairness guarantees. Your testbed will be one of the largest and most demanding networked systems there is: the electricity distribution grid, where thousands of solar panels, heat pumps and electric vehicles compete for limited network capacity. If you enjoy turning hard real-world problems into elegant mathematics, this position is for you.

Note to applicants: No background in energy systems is required; you will pick up the domain knowledge during the project, together with our energy experts. We are looking for candidates with a strong background in control systems, optimization or applied mathematics.

Job Description
At its core, this project tackles a fundamental problem in networked control: how to allocate a scarce shared resource among many agents in a way that is efficient, scalable and fair, using only local computation and limited communication. The motivating application is congestion management in electricity distribution networks. As rooftop photovoltaics, heat pumps and electric vehicles spread, local networks increasingly hit their capacity limits, and operators must decide how much power each prosumer may inject or consume. The core idea is to embed clear and interpretable notions of fairness directly into distributed control and optimization algorithms, as constraints or objectives with provable guarantees.

The project advances fundamental science at the intersection of distributed optimization, control theory, multi-agent systems and machine learning. It seeks to redefine how societal objectives such as fairness are formalized mathematically and enforced in intelligent control systems.

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.

You will work closely with experts in distributed control, responsible AI and power systems across departments, who provide the domain knowledge and real-world data to test your methods.

Beyond the theory, your algorithms will help distribution system operators manage congestion transparently and equitably, speeding up the integration of renewable energy and strengthening public trust in the energy transition.

As a PhD candidate, you will:

• Formulate fair resource allocation problems: Cast congestion management as a large-scale, constrained multi-agent optimization problem, and formalize interpretable fairness notions (e.g., proportional, max-min or probabilistic fairness) as mathematical constraints or objectives.
• Design distributed optimization algorithms: Develop and analyze consensus-based, primal-dual, ADMM-type and game-theoretic algorithms that coordinate thousands of agents using only local information and limited communication.
• Develop fairness-aware feedback controllers: Design distributed controllers (e.g., distributed MPC, online feedback optimization) that enforce fairness while guaranteeing scalability, stability and constraint satisfaction.
• Analyze probabilistic allocation policies: Study randomized and weighted allocation schemes, such as raffle-based curtailment, and characterize their fairness and efficiency over time.
• Handle uncertainty: Incorporate stochastic models of generation and demand using tools from stochastic, robust and learning-based control.
• Prove theoretical guarantees: Establish convergence, stability, robustness and fairness guarantees, including under forecasting errors, model mismatch and communication delays.
• Validate on realistic large-scale networks: Test your algorithms in simulations of realistic distribution networks and on real-world data from Dutch distribution system operators.
• Publish and collaborate: Publish in leading control and optimization venues (e.g., IEEE TAC, Automatica, CDC) and work with colleagues in control, AI and electrical engineering.

This PhD position is part of the project "Responsible AI for Fair and Efficient Control of Energy Systems (RAICES)“ (https://www.tue.nl/en/research/institutes/eindhoven-artificial-intelligence-systems-institute/ai-research/eaisi-emdair-program)

Job Requirements

• A master’s degree (or an equivalent university degree) in Systems and Control, Electrical Engineering, Mechanical Engineering, Applied Mathematics, Operations Research or a related field.
• In case you are still doing your MSc in the field listed above, please indicate your expected graduation date in the cover letter.
• A strong background in control theory and/or optimization (e.g., convex or distributed optimization, multi-agent systems, model predictive control, game theory).
• Solid mathematical skills and enjoyment of rigorous analysis and proofs.
• Programming experience (e.g., Python, MATLAB or Julia).
• A research-oriented attitude with curiosity.
• Ability to work in an interdisciplinary team.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Prior knowledge of power or energy systems is not required.

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 Dr. Giulia De Pasquale (g.de.pasquale@tue.nl) or Dr. Nikolaos Paterakis (n.paterakis@tue.nl).

Visit our website for more information about the application process. You can also contact HRServices.ee@tue.nl.

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.

Robotics Technology Center Engineer

You will develop and maintain high-quality demonstrators (TRL 4-6) of robotic systems that showcase the value of scientific developments in robotics (TRL 2-4). This includes implementing methods from state-of-the-art robotics research and translating them into stable, robust, and professional demonstrators that can be witnessed and used by/with industrial partners. Although part of your role will be based on 2- to 3-month, goal-oriented projects with industry, you are responsible for consolidating results/knowledge back into a common stack and/or infrastructure.

The Robotics Technology Center
The RTC aims to accelerate the adoption of advanced robotics by translating TU/e research into impactful demonstrators and prototypes that showcase opportunities, reduce innovation barriers, and create value for industry and society. Our approach is to develop pre-competitive demonstrators together with researchers and industry partners, validate innovations in realistic environments, and advance technologies along their pathway to societal and economic impact.

The RTC enables industry to validate advanced robotic technology through a test-before-invest approach: using state-of-the-art robotics research outputs, university infrastructure, and robotic platforms, potentially provided by industry. This results in a recommendation regarding task automation and a referral to the appropriate ecosystem partner for commercial follow-up..

Key Responsibilities
In this role, you will:

• Design, develop, and deploy robotic demonstrators based on TU/e research outcomes.
• Translate cutting-edge robotics research into compelling demonstrators that address real industrial challenges.
• Integrate and implement new software, algorithms, and control solutions into existing robotic platforms.
• Work with a broad range of robotic systems and laboratory infrastructure within the Mechanical Engineering department and industry platforms.
• Collaborate closely with researchers, engineers, and industry partners to define demonstrator requirements and use cases.
• Validate robotic solutions in realistic application environments and support technology maturation towards industrial adoption.
• Maintain and continuously improve demonstrators to ensure reliability, robustness, and professional presentation quality.
• Keep demonstrator software up to date and ensure compatibility with evolving research and platform developments.
• Support demonstrations, workshops, and industry visits by showcasing robotic technologies and explaining their capabilities and potential impact.