PhD Position Electrochemical Purification and Recovery from Mixed Li-ion Battery Waste

Job description
Join the Materials Production, Refining and Recycling (MPRR) group in the Department of Materials Science and Engineering (MSE) at TU Delft, in collaboration with the Netherlands Organisation for Applied Scientific Research (TNO), to contribute to advancing Li-ion battery circularity by developing innovative methods to electrochemical the purification and separation of various elements from mixed NMC and LFP Li-ion battery waste streams.

The project will be jointly supervised by Dr. Shoshan Abrahami (TU Delft) and Dr. Devin Boom (TNO), and is part of the ADAPT-BATT (Adaptive Processing for LFP-mixed Battery Waste Streams) project. ADAPT-BATT is embedded within the national Material Independence & Circular Batteries program, which supports the growth of the Li-ion battery manufacturing and recycling industry in the Netherlands.

As a PhD researcher, you will:

• Optimize the leaching of desired elements from mixed battery waste streams.
• Develop sustainable processes to electrify the purification and separation methods of Cu, Al, Fe, graphite from the leach solution.
• Optimize cell design and closed loop operation.
• Evaluate the performance of recycled materials in new battery cells.

Your work will contribute to recycling routes that use fewer chemicals, reduce waste, and lower greenhouse gas emissions compared to conventional recycling processes.

Background
Batteries are essential for decarbonizing transport and enabling renewable energy storage, yet their widespread use presents a growing waste-management challenge. Recycling spent batteries is key to recovering valuable materials and improving the sustainability of the battery value chain. Hydrometallurgical recycling is especially well-suited for processing the complex and variable composition of industrial Li-ion battery waste streams, including LFP chemistries. However, to meet EU regulatory targets and reduce environmental impact, these processes must become more efficient and selective to enable high purity recovery and improved recovery rates.

Job requirements
We are seeking a proactive, self-motivated candidate with a strong background in experimental research. Candidates should meet the following requirements:

• MSc degree in Materials Science, Metallurgy, Chemistry, or a closely related field.
• Practical experience in hydrometallurgy and electrochemistry. Previous work on lithium battery recycling in a plus.
• Proven hands-on experience working in a chemical laboratory and strong knowledge of safety procedures and risk management practices.
• Solid understanding of chemical reactions and thermodynamics. Strong communication skills in English, including proficiency in scientific writing (reports, papers) and technical presentations. TU Delft requires a certain level of English proficiency for participation in Doctoral Education courses and for writing the PhD thesis and scientific publications. Please consult the Graduate School’s Admission Requirements for details.
• Ability to work effectively in a team and willingness to collaborate with project partners, including academic and industry stakeholders.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Mechanical Engineering
From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

PhD Position Electrochemical Purification and Recovery from Mixed Li-ion Battery Waste

Job description
Join the Materials Production, Refining and Recycling (MPRR) group in the Department of Materials Science and Engineering (MSE) at TU Delft, in collaboration with the Netherlands Organisation for Applied Scientific Research (TNO), to contribute to advancing Li-ion battery circularity by developing innovative methods to electrochemical the purification and separation of various elements from mixed NMC and LFP Li-ion battery waste streams.

The project will be jointly supervised by Dr. Shoshan Abrahami (TU Delft) and Dr. Devin Boom (TNO), and is part of the ADAPT-BATT (Adaptive Processing for LFP-mixed Battery Waste Streams) project. ADAPT-BATT is embedded within the national Material Independence & Circular Batteries program, which supports the growth of the Li-ion battery manufacturing and recycling industry in the Netherlands.

As a PhD researcher, you will:

• Optimize the leaching of desired elements from mixed battery waste streams.
• Develop sustainable processes to electrify the purification and separation methods of Cu, Al, Fe, graphite from the leach solution.
• Optimize cell design and closed loop operation.
• Evaluate the performance of recycled materials in new battery cells.

Your work will contribute to recycling routes that use fewer chemicals, reduce waste, and lower greenhouse gas emissions compared to conventional recycling processes.

Background
Batteries are essential for decarbonizing transport and enabling renewable energy storage, yet their widespread use presents a growing waste-management challenge. Recycling spent batteries is key to recovering valuable materials and improving the sustainability of the battery value chain. Hydrometallurgical recycling is especially well-suited for processing the complex and variable composition of industrial Li-ion battery waste streams, including LFP chemistries. However, to meet EU regulatory targets and reduce environmental impact, these processes must become more efficient and selective to enable high purity recovery and improved recovery rates.

Job requirements
We are seeking a proactive, self-motivated candidate with a strong background in experimental research. Candidates should meet the following requirements:

• MSc degree in Materials Science, Metallurgy, Chemistry, or a closely related field.
• Practical experience in hydrometallurgy and electrochemistry. Previous work on lithium battery recycling in a plus.
• Proven hands-on experience working in a chemical laboratory and strong knowledge of safety procedures and risk management practices.
• Solid understanding of chemical reactions and thermodynamics. Strong communication skills in English, including proficiency in scientific writing (reports, papers) and technical presentations. TU Delft requires a certain level of English proficiency for participation in Doctoral Education courses and for writing the PhD thesis and scientific publications. Please consult the Graduate School’s Admission Requirements for details.
• Ability to work effectively in a team and willingness to collaborate with project partners, including academic and industry stakeholders.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty Mechanical Engineering
From chip to ship. From machine to human being. From idea to solution. Driven by a deep-rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.

ME is a dynamic and innovative faculty with high-tech lab facilities and international reach. It’s a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME’s outstanding, state-of-the-art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well-being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

2 PhD Positions in Short-Term and Long-Term Transport Planning under Uncertainty

Job description
The scientific challenge

Urban transport systems generate ever-growing data streams, yet they continue to fail during disruptions. One key reason is that short-term operations and long-term planning are designed in silos. As a result, supply and demand adjustments occur out of sync, leading to congestion, inefficiencies, and service breakdowns.

The ERC consolidator project TRANSFORM addresses this gap by developing a unified framework for resilient multimodal systems under uncertainty. The project reframes multimodal mobility as a coupled system with three interacting players, mobility service suppliers, infrastructure operators, and users whose decisions and reactions unfold on different time scales. What makes TRANSFORM distinctive is the way it fuses dynamic uncertainty modeling, behaviorally informed demand management, and iterative optimization across multiple decision layers into one coherent methodology.

Your Research Role

We are recruiting two PhD candidates, each focusing on a different but closely connected layer of this challenge.

PhD Position 1: Short-Term Multimodal Transport Planning under Uncertainty
This position focuses on operational decision-making in dynamic and disrupted environments.

You will:

• Develop next-generation short-term multimodal supply management models under deep uncertainty
• Integrate estimation and combinatorial optimization for large-scale fleet scheduling and service coordination
• Design AI-driven, tractable optimization methods for real-time decision support
• Develop scalable algorithms suitable for high-dimensional, capacity-constrained transport networks

This role would be a great fit for you if you have strong foundations in machine learning (for example causal inference or predictive modelling) and an interest in combinatorial optimization, or vice versa. Experience with simulation modelling is a plus.

PhD Position 2: Long-Term Multimodal Transport Planning under Uncertainty
This position focuses on strategic planning and infrastructure adaptation.

You will:

• Develop robust network expansion and adaptation models under deep and structural uncertainty
• Design new uncertainty quantification approaches for unobserved, heavy-tailed, and cascading disruption effects
• Integrate causal reasoning into large-scale combinatorial optimization for infrastructure planning
• Deliver methods that are both scientifically novel and deployable in real-world strategic planning contexts

This role would be a great fit for you if you have strong foundations in AI-driven modelling and large-scale optimization, and an interest in uncertainty modelling and strategic systems design, or vice versa.

Where you will work

Your home base will be the SUM Lab in the Department of Transport & Planning (T&P) within the Faculty of Civil Engineering and Geosciences. Our diverse team of researchers, project managers, and professors shares the ambition to create smart, sustainable, and equitable mobility systems. T&P consists of 12 collaborative labs applying advanced technologies such as sensing, data analytics, modelling, and AI to turn scientific research into real-world impact. You will work closely with domain experts Yanan Xin, Ludovic Leclercq, Yousef Maknoon and Oded Cats, and collaborate with fellow PhD colleagues and researchers across behavioral modelling, optimization, and transport systems analysis.

The position is embedded in a prestigious ERC consolidator grant, offering strong scientific visibility and opportunities for international collaboration.

Job requirements

• A Master's degree in a relevant field, i.e. Operations research, Applied mathematics, Machine Learning or Computer science. Engineering degree with strong methodological backgrounds is considered as well.
• Strong background in machine learning (e.g., predictive modelling, causal inference, or uncertainty quantification) and/or combinatorial optimization (mathematical modelling, decomposition methods, heuristics, metaheuristic).
• Advanced programming skills (e.g. Python, C++ or Java).
• Ability to work both in a project team, but also independently and take leadership and responsibility for research tasks.
• Interest in interdisciplinary collaboration and contributing to teaching activities.
• Excellent communication skills in English, both written and oral.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Civil Engineering and Geosciences
The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

PhD Positioin in Multimodal Demand Management and Optimization under Uncertainty

Job description
The scientific challenge

Urban transport systems generate ever-growing data streams, yet they continue to fail during disruptions. One key reason is that short-term operations and long-term planning are designed in silos. As a result, supply and demand adjustments occur out of sync, leading to congestion, inefficiencies, and service breakdowns.

The ERC Consolidator project TRANSFORM addresses this gap by developing a unified framework for resilient multimodal systems under uncertainty. The project reframes multimodal mobility as a coupled system with three interacting players, mobility service suppliers, infrastructure operators, and users whose decisions and reactions unfold on different time scales. What makes TRANSFORM distinctive is the way it fuses dynamic uncertainty modeling, behaviorally informed demand management, and iterative optimization across multiple decision layers into one coherent methodology.

Your research role

In this PhD position you will develop the scientific core that enables real-time, coordinated multimodal demand management. You will design a modelling and optimization framework that:

• Explicitly captures the dynamic feedback loop between supply and demand
• Integrates behavioral choice models with network state information
• Supports forward-looking optimization under uncertainty
• Designs individualized multimodal services that improve efficiency while maintaining service quality and user preferences

Your work will result in a novel, scalable modelling framework that advances both theory and application in resilient multimodal transport systems.

Where you will work

Your home base will be the SUM Lab in the Department of Transport & Planning (T&P) within the Faculty of Civil Engineering and Geosciences. Our diverse team of researchers, project managers, and professors shares the ambition to create smart, sustainable, and equitable mobility systems. T&P consists of 12 collaborative labs applying advanced technologies such as sensing, data analytics, modelling, and AI to turn scientific research into real-world impact. You will work closely with domain experts Bilge Atasoy and Maarten Kroesen, and collaborate with fellow PhDs and researchers across behavioral modelling, optimization, and transport systems analysis.

The position is embedded in a prestigious ERC consolidator grant, offering strong scientific visibility and opportunities for international collaboration.

Job requirements

• A Master's degree in a relevant field, i.e. Applied mathematics, Machine Learning, or Computer science. Engineering degree with strong methodological backgrounds related to these topics is considered as well.
• Solid knowledge of machine learning, optimization, and discrete choice modelling/ behavioral models.
• Strong programming skills (e.g. Python, C++, Java).
• Ability to work both in a project team, but also independently and take leadership and responsibility for research tasks.
• Interest in interdisciplinary collaboration and contributing to teaching and lab activities.
• EExcellent communication skills in English, both written and oral .  

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Civil Engineering and Geosciences
The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

2 PhD Positions in Short-Term and Long-Term Transport Planning under Uncertainty

Job description
The scientific challenge

Urban transport systems generate ever-growing data streams, yet they continue to fail during disruptions. One key reason is that short-term operations and long-term planning are designed in silos. As a result, supply and demand adjustments occur out of sync, leading to congestion, inefficiencies, and service breakdowns.

The ERC consolidator project TRANSFORM addresses this gap by developing a unified framework for resilient multimodal systems under uncertainty. The project reframes multimodal mobility as a coupled system with three interacting players, mobility service suppliers, infrastructure operators, and users whose decisions and reactions unfold on different time scales. What makes TRANSFORM distinctive is the way it fuses dynamic uncertainty modeling, behaviorally informed demand management, and iterative optimization across multiple decision layers into one coherent methodology.

Your Research Role

We are recruiting two PhD candidates, each focusing on a different but closely connected layer of this challenge.

PhD Position 1: Short-Term Multimodal Transport Planning under Uncertainty
This position focuses on operational decision-making in dynamic and disrupted environments.

You will:

• Develop next-generation short-term multimodal supply management models under deep uncertainty
• Integrate estimation and combinatorial optimization for large-scale fleet scheduling and service coordination
• Design AI-driven, tractable optimization methods for real-time decision support
• Develop scalable algorithms suitable for high-dimensional, capacity-constrained transport networks

This role would be a great fit for you if you have strong foundations in machine learning (for example causal inference or predictive modelling) and an interest in combinatorial optimization, or vice versa. Experience with simulation modelling is a plus.

PhD Position 2: Long-Term Multimodal Transport Planning under Uncertainty
This position focuses on strategic planning and infrastructure adaptation.

You will:

• Develop robust network expansion and adaptation models under deep and structural uncertainty
• Design new uncertainty quantification approaches for unobserved, heavy-tailed, and cascading disruption effects
• Integrate causal reasoning into large-scale combinatorial optimization for infrastructure planning
• Deliver methods that are both scientifically novel and deployable in real-world strategic planning contexts

This role would be a great fit for you if you have strong foundations in AI-driven modelling and large-scale optimization, and an interest in uncertainty modelling and strategic systems design, or vice versa.

Where you will work

Your home base will be the SUM Lab in the Department of Transport & Planning (T&P) within the Faculty of Civil Engineering and Geosciences. Our diverse team of researchers, project managers, and professors shares the ambition to create smart, sustainable, and equitable mobility systems. T&P consists of 12 collaborative labs applying advanced technologies such as sensing, data analytics, modelling, and AI to turn scientific research into real-world impact. You will work closely with domain experts Yanan Xin, Ludovic Leclercq, Yousef Maknoon and Oded Cats, and collaborate with fellow PhD colleagues and researchers across behavioral modelling, optimization, and transport systems analysis.

The position is embedded in a prestigious ERC consolidator grant, offering strong scientific visibility and opportunities for international collaboration.

Job requirements

• A Master's degree in a relevant field, i.e. Operations research, Applied mathematics, Machine Learning or Computer science. Engineering degree with strong methodological backgrounds is considered as well.
• Strong background in machine learning (e.g., predictive modelling, causal inference, or uncertainty quantification) and/or combinatorial optimization (mathematical modelling, decomposition methods, heuristics, metaheuristic).
• Advanced programming skills (e.g. Python, C++ or Java).
• Ability to work both in a project team, but also independently and take leadership and responsibility for research tasks.
• Interest in interdisciplinary collaboration and contributing to teaching activities.
• Excellent communication skills in English, both written and oral.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

Faculty of Civil Engineering and Geosciences
The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.