The Partnership Sustainable Process Engineering consists of the four groups Chemical Reactor Engineering headed by Prof.dr.ir. John van der Schaaf, the group on biomass processing of Dr. Neira d’Angelo, the group Energy Intensified Chemical Reaction Engineering of Prof. Dr. Evgeny Rebrov and the Inorganic Membranes & Membrane Reactors group of Prof.dr.Eng. Fausto Gallucci.
The SPE partnership consists of 3 Professors and 1UD, (all acting as PIs), 15 postdocs, more than 55 PhD students and 7 support staff.
The group's mission is to be among the world's top academic research groups in its field and to be leading in the development of novel technologies for new, highly efficient, inherently safe, and robust (micro)structured multiphase processing systems, which show the best productivity by a dedicated design of all relevant dimensions and optimum choice of dedicated operational procedures.
The field of Process Systems Engineering (PSE) is at the heart of addressing some of the most pressing challenges associated with the global energy and material transition. As industries face mounting pressure to reduce their environmental impact and adopt sustainable practices, PSE plays a pivotal role in designing, optimizing, and managing complex industrial processes to improve efficiency, reduce waste, and minimize resource consumption. In the context of the energy transition, the demand for renewable energy sources, decarbonization strategies, and energy-efficient systems has increased the complexity of process engineering, requiring advanced, data-driven approaches to meet these evolving needs.
To transition to a low-carbon future, industries must rethink process designs and adopt technologies that allow for cleaner and more efficient production. Machine learning can support this shift by offering powerful tools to model, simulate, and optimize processes in ways that were previously unachievable, enabling faster innovation cycles and the deployment of carbon reducing solutions.
In parallel, there is an urgent need for process intensification, which focuses on creating processes that deliver the same or better outputs with significantly less energy, space, and resource input. This is especially critical in achieving the goals of the Paris Agreement and other climate initiatives, which call for rapid reductions in global emissions.
By implementing intensified processes, such as those involving membrane separation, modular reactors, or integrated heat and mass transfer systems, industries can drastically cut down on their energy usage and environmental footprint. The integration of machine learning into PSE is transforming how these intensified processes are designed and managed, offering new ways to analyse large datasets, optimize complex systems, and adjust operations in real time to improve efficiency and sustainability.
Another significant challenge is developing closed-loop, circular production systems that minimize waste and recycle resources. With finite natural resources and the environmental toll of waste disposal, industries are increasingly embracing circularity to promote resource efficiency and reduce waste generation. PSE, combined with machine learning, enables the development of predictive models that can optimize recycling processes, minimize raw material usage, and enable efficient waste valorization. In particular, the role of sustainable separation processes and membrane technology is vital, as they allow for the selective recovery of valuable materials and the purification of effluents in a resource-efficient manner.
The Assistant Professor we are seeking will address these challenges by combining machine learning techniques with PSE to drive innovation in sustainable, energy-efficient processes.
His/her work will align with the energy transition goals, focusing on designing systems that reduce energy consumption, cut emissions, and improve overall sustainability. Through their research, the candidate will contribute to the development of practical solutions that facilitate cleaner, more sustainable production methods across various sectors, thus playing an essential role in the shift toward a more sustainable, circular economy.
Key Responsibilities:
1. Research and Development in Process Systems Engineering and Machine Learning: The successful candidate will conduct high-impact research that leverages machine learning techniques to solve complex process engineering challenges. Their research will focus on modeling, optimizing, and controlling systems to enhance performance, efficiency, and sustainability. By applying data-driven and AI-based approaches, the candidate will develop methodologies that improve process understanding and operation, ultimately enabling more sustainable and economically viable industrial practices.
2. Focus on Sustainability and Circular Economy: A core component of the role involves aligning research with sustainability principles. The candidate will work on minimizing waste, reducing energy consumption, and maximizing resource efficiency through innovative process designs. Their expertise will support the transition from linear production models to circular systems, where resources are continuously reused and recycled. This focus on circularity will be instrumental in reducing the environmental footprint of industrial processes, aligning with both industry and societal goals for sustainable development.
3. Advancements in Process Intensification: Process intensification aims to make processes more efficient by reducing equipment size, improving throughput, and minimizing waste. The candidate’s research will explore ways to intensify processes using machine learning techniques to design systems that are more compact, energy-efficient, and productive. By incorporating real-time data analysis and optimization, their work will drive innovations in processes like reactive distillation, advanced heat exchangers, and membrane reactors, which are critical for industries seeking higher productivity with lower environmental impact.
4. Sustainable Separation Processes and Membrane Technology: Separation processes are integral to many industrial applications, from wastewater treatment to chemical production. The candidate’s expertise in separation science, especially membrane-based processes, will be critical for developing more sustainable and efficient solutions. Research will focus on designing and optimizing membranes with machine learning to enhance selectivity, permeability, and durability, addressing challenges in separating valuable components from complex mixtures. The candidate will also explore sustainable materials for membrane fabrication, contributing to a reduced dependency on conventional, resource-intensive materials.
5. Teaching and Mentorship: In addition to research, the Assistant Professor will be responsible for teaching graduate and undergraduate courses in process systems engineering, machine learning for process optimization, and sustainable Process Design. He/she will inspire and mentor students, fostering the next generation of engineers equipped with the skills to tackle pressing environmental and industrial challenges. Their teaching philosophy will emphasize hands-on, data-driven learning, ensuring students are well-prepared to apply machine learning in practical settings.
6. Industry and Community Engagement: The ideal candidate will actively engage with industry partners, fostering collaborations that facilitate the translation of research into practical solutions. By working with stakeholders in chemical manufacturing, water treatment, and other process-intensive sectors, the candidate will address real-world problems, leading to sustainable and efficient industrial practices. Additionally, they will participate in community outreach, promoting awareness of sustainability, circularity, and the role of machine learning in shaping the future of process engineering.
7. Grant Writing and Research Funding: The Assistant Professor will be expected to pursue external funding to support their research initiatives. This includes applying for grants from national and international research bodies that prioritize sustainable development, advanced manufacturing, and AI-driven innovations. Securing research funding will enable the establishment of a well-equipped lab for process systems engineering, where experimental and computational studies can be conducted to validate and enhance theoretical models.
We are looking for a motivated researcher with:
This position offers a unique opportunity to join a forward-thinking team committed to sustainable engineering solutions for circularity and sustainability. The successful candidate will contribute to advancing knowledge at the intersection of process engineering and AI, supporting a sustainable future through groundbreaking research and impactful teaching.
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:
About us
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.
Curious to hear more about what it is like as a professor at TU/e? Please view the video: https://www.youtube.com/watch?v=85ApbAfeCcM
Information
Do you recognize yourself in this profile and would you like to know more?
Please contact prof.dr. Fausto Gallucci, f.gallucci@tue.nl.
Visit our website for more information about the application process or the conditions of employment. You can also contact Mrs. Sandra van de Weijer, HR advisor, p.j.v.d.weijer@tue.nl.
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.
The application should include a:
We look forward to receiving your application and will screen it as soon as possible.