Postdoc in Physicochemical Processes in Sediment Depots for Resource Recovery

Job description

Sediment depots contain complex and heterogeneous mixtures of contaminated dredged sediments with significant potential for resource recovery. While historical records of deposited materials exist, there is currently limited understanding of how these sediments and associated pollutants have evolved under the unique environmental conditions within the depot. The system is highly stratified and poorly mixed, leading to strong spatial variability in physicochemical and biological processes, and consequently large uncertainties in pollutant distribution, transformation, and resource potential.

A key knowledge gap concerns the role of indigenous microbial communities in contaminant degradation and transformation. Understanding whether and where biodegradation occurs is essential for predicting long-term environmental risks and identifying opportunities for microbial treatment and resource recovery. At the same time, there is an urgent need to develop strategies for sediment management, purification, and capacity maintenance.

This postdoctoral project aims to establish a comprehensive geochemical characterization of the sediments and associated pollutants. The work will provide insights into contaminant concentrations, microbial activity, vertical heterogeneity, and hotspot zones, forming a critical baseline for evaluating treatment strategies and enabling beneficial reuse of materials.

The postdoc will focus on physicochemical processes and technology development for potential valuable resource isolation and recovery. This includes evaluating and advancing existing techniques such as electrochemical extraction, chemical and bio-leaching, adsorption, and physical separation. A key objective is to assess the effectiveness of current technologies, identify limitations, and define pathways for their improvement or redesign in the context of complex sediment matrices.

The research will be conducted in close collaboration with a PhD candidate working on pollutant identification and microbial processes, within the framework of the Contaminated Sludge to Resource project, part of the Haskoning Innovation & Education Fund and co-funded by the TKI ChemistryNL research programme.

The project will integrate long-term monitoring data with new sediment core analyses to assess both known and emerging contaminants, including their spatial distribution. Analytical techniques such as grain size analysis, X-ray diffraction, and ICP-MS and/or ICP-OES will be used to characterize sediment composition, mineralogy, metal distribution, and mobility.

The postdoc will develop an integrated roadmap combining physicochemical and microbial approaches for sustainable sediment management and resource recovery. This work will contribute to a new interdisciplinary research line, established through interdepartmental collaboration, at the interface of biogeochemistry, microbiology, and engineering, with direct relevance for large-scale sediment management practices. The position is embedded in the Department of Hydraulic Engineering at TU Delft, internationally recognised for research and education.

Job requirements

• PhD in chemical engineering, materials science, environmental engineering, geochemistry or a related field.
• Experience with laboratory testing of materials, preferably soils, sediments, or construction materials.
• Ability to design and perform experimental work, analyse results, and report findings clearly.
• Strong analytical skills and the ability to work independently within a multidisciplinary team.
• Affinity with environmental biogeochemistry.
• Knowledge of circular materials and separation techniques applied to metal recovery is an advantage.
• Excellent command of written and spoken English, as you will be working in an international environment.  

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.

Postdoc in Physicochemical Processes in Sediment Depots for Resource Recovery

Job description

Sediment depots contain complex and heterogeneous mixtures of contaminated dredged sediments with significant potential for resource recovery. While historical records of deposited materials exist, there is currently limited understanding of how these sediments and associated pollutants have evolved under the unique environmental conditions within the depot. The system is highly stratified and poorly mixed, leading to strong spatial variability in physicochemical and biological processes, and consequently large uncertainties in pollutant distribution, transformation, and resource potential.

A key knowledge gap concerns the role of indigenous microbial communities in contaminant degradation and transformation. Understanding whether and where biodegradation occurs is essential for predicting long-term environmental risks and identifying opportunities for microbial treatment and resource recovery. At the same time, there is an urgent need to develop strategies for sediment management, purification, and capacity maintenance.

This postdoctoral project aims to establish a comprehensive geochemical characterization of the sediments and associated pollutants. The work will provide insights into contaminant concentrations, microbial activity, vertical heterogeneity, and hotspot zones, forming a critical baseline for evaluating treatment strategies and enabling beneficial reuse of materials.

The postdoc will focus on physicochemical processes and technology development for potential valuable resource isolation and recovery. This includes evaluating and advancing existing techniques such as electrochemical extraction, chemical and bio-leaching, adsorption, and physical separation. A key objective is to assess the effectiveness of current technologies, identify limitations, and define pathways for their improvement or redesign in the context of complex sediment matrices.

The research will be conducted in close collaboration with a PhD candidate working on pollutant identification and microbial processes, within the framework of the Contaminated Sludge to Resource project, part of the Haskoning Innovation & Education Fund and co-funded by the TKI ChemistryNL research programme.

The project will integrate long-term monitoring data with new sediment core analyses to assess both known and emerging contaminants, including their spatial distribution. Analytical techniques such as grain size analysis, X-ray diffraction, and ICP-MS and/or ICP-OES will be used to characterize sediment composition, mineralogy, metal distribution, and mobility.

The postdoc will develop an integrated roadmap combining physicochemical and microbial approaches for sustainable sediment management and resource recovery. This work will contribute to a new interdisciplinary research line, established through interdepartmental collaboration, at the interface of biogeochemistry, microbiology, and engineering, with direct relevance for large-scale sediment management practices. The position is embedded in the Department of Hydraulic Engineering at TU Delft, internationally recognised for research and education.

Job requirements

• PhD in chemical engineering, materials science, environmental engineering, geochemistry or a related field.
• Experience with laboratory testing of materials, preferably soils, sediments, or construction materials.
• Ability to design and perform experimental work, analyse results, and report findings clearly.
• Strong analytical skills and the ability to work independently within a multidisciplinary team.
• Affinity with environmental biogeochemistry.
• Knowledge of circular materials and separation techniques applied to metal recovery is an advantage.
• Excellent command of written and spoken English, as you will be working in an international environment.  

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 Position Model Based Control of Soft Robots

Job description
We are looking for a motivated PhD candidate to join the Physical Intelligence Lab at TU Delft and contribute to advancing model-based control of soft robots. The position is funded by the ERC Starting Grant “RIPLEY,” led by Dr. Cosimo Della Santina. The project aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object.

Unlike traditional robots, which rely on rigid links and localized interaction points, soft robots are composed of deformable materials that can conform to complex geometries and operate safely around humans. They offer unique advantages for manipulation and locomotion, but current control algorithms do not fully exploit their capabilities. Most rely on approximations tailored for rigid systems or require extensive sensing and computation. This project addresses the challenge of developing model-based control strategies tailored to soft bodies, capable of exploiting their compliant nature.

The PhD candidate will focus on the theoretical and algorithmic development of control methods that combine physical modeling and real-time computation. The work will involve deriving reduced-order models, designing controllers that exploit the system’s distributed dynamics, and validating them experimentally on soft robotic platforms in our lab. These include tentacle-like manipulators and soft arms, eventually leading toward autonomous, multi-limb systems for tasks such as inspection, agriculture, and healthcare.

The PhD student will be supervised by Dr. Della Santina and work closely with a team of researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading international conferences and to collaborate with other academic and industrial partners.

The ideal candidate holds a Master’s degree in Robotics, Control Theory, Mechanical Engineering, or Applied Mathematics. A background in system dynamics, modeling, or control is essential; prior experience with soft robotics or experimental work is a plus but not required. We value independence, curiosity, and a willingness to bridge theory and practice.

The position is full-time and based in Delft, The Netherlands. TU Delft offers a stimulating and supportive research environment, competitive salary, and broad training opportunities for career development.

Job requirements

• First and foremost: scientific curiosity!
• A Master’s degree (or equivalent) in Robotics, Control Theory, Mechanical Engineering, Applied Mathematics, or a related field.
• Solid background in dynamical systems, modeling, and control.
• Some programming experience.
• Strong analytical mindset and problem-solving attitude.
• Good communication skills and the ability to work both independently and in a team.
• Proficiency in written and spoken English.
• Prior experience in soft robotics, machine learning for control, or experimental robotics is a plus, but not required.
• Affinity with hands-on work and willingness to interact with physical robotic platforms is a plus, but not required.

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 Causal Inference & Machine Learning

Job description

Progress in statistics and machine learning research has led to great strides in using data to uncover relationships between things in the world we observe around us. Many questions of scientific and societal (e.g. medical) importance, however, are causal questions. Causal questions ask what the effects of new actions will be, rather than how things are related in a world in which we keep things as is.

Given the importance of answering such causal questions, many data-driven methods have been developed to estimate causal effects in machine learning as well as statistics, epidemiology, econometrics, psychometrics, and many others. However, these methods all rely on causal assumptions, such as positivity and unconfoundedness. When such assumptions are violated, these methods will return incorrect causal answers, which leads to flawed and potentially even harmful decision-making. Currently, however, there is a lack of data-driven methods to detect assumption violations, leading to problems that go undetected, and ultimately, untrustworthy causal claims.

In this project, our goal is to better understand the limits of the detection of causal assumption violations, develop new detection methods, incorporate these violations into causal models, and design procedures to mitigate their influence. By offering these tools to practitioners, we will contribute to a new, safer approach to addressing causal questions that leads to more trustworthy causal answers.

The project particularly focusses on positivity violations, that is the assumption that it is possible for each treatment of interest to have occurred for each relevant unit in the data. We are interested in developing methods to detect whether this assumption is violated, to find out for which treatments or subpopulations it holds, and to properly account for the uncertainty caused by limited support in the data. We want to investigate this in both simple and more complicated (e.g. high-dimensional or longitudinal) settings.

The project is part of the "Safe Causal Inference" consortium, a multi-disciplinary (computer science, mathematics, biostatistics, epidemiology) consortium spanning eight PhD positions to improve the trustworthiness of causal methods. Through the consortium, you will be able to collaborate with experts and peers from multiple causal inference disciplines to strengthen and inspire your work.

We are looking for an enthusiastic and self-motivated person with a passion for research. We offer a stimulating scientific research environment and the embedding in a multi-disciplinary consortium that allows you to develop skills that go beyond discipline-specific boundaries. You will be supervised by Jesse Krijthe (computer science, TU Delft) and Nan van Geloven (biostatistics, Leiden University Medical Center) and be primarily based in the Pattern Recognition and Bioinformatics group of TU Delft, which includes researchers working on the methodology of machine learning, bioinformatics, computer vision, and socially perceptive computing.

Job requirements

• A critical thinker with an open mind
• Strong interest in understanding and developing statistical and machine learning methods
• Master’s degree in a relevant quantitative field (such as statistics, computer science, mathematics, econometrics, psychometrics, physics, etc.)
• Ability to program in Python, R, Julia or a similar (scientific) programming language
• Proficient in spoken and written English

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 Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.

Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.

PhD Position Composition of Multidisciplinary Simulation Workflows for Collaborative Engineering

Job description

This PhD position is part of the Dutch Aviation Systems Analysis Lab (DASAL project, a flagship collaboration between TU Delft and Royal NLR, funded through the Dutch national growth fund under the ‘Luchtvaart in Transitie’ programme.

DASAL serves as a living lab for developing integrated models and simulations to support innovation throughout the Dutch aviation sector and public policy. Within this unique ecosystem, you will join a diverse and interdisciplinary team of PhD candidates and postdocs from across the Faculty of Aerospace Engineering, collaborating closely with experts at NLR and the Ministry of Infrastructure and Water Management to analyse the complex interactions between sustainability, economics, and societal impact in aviation.

You will focus on the challenges associated with the uncertainty under which future sustainable aircraft are designed. In fact, a central challenge in integrating multiple modeling components into a dgital thread is that the inherent complexity hides the aggregate effects and interactions of component-level parameters and modeling approximations, thereby obscuring the key drivers of compulative errors that jeopartize the compliance with constraints and requirements. You will work on the definition of methods for the stochastic modeling approach, implemented non-intrusively in the digital thread of the project DASAL, according to three main pillars:

1. Uncertainty propagation: understanding modeling and design uncertainties at different component levels and how these propagate throughout the aggregate global system, leading to traceable confidence intervals on KPIs.
2. Global sensitivity analysis: Identify components and interations between them that are responsible for the majority of the effects on the KPIs, allowing weak links in the system-model to be identified and mitigated.
3. Inversion and robust design: given target confidence in KPIs, quantify the accuracy required in system sub-components and parameters needed to achieve that accuracy.

The stochastic MBSE/KBE framework created will be demonstrated on the use case of a next-generation medium-haul hydrogen aircraft.

You will be based at the Aerodynamics group of the Faculty of Aerospace Engineering, and strongly cooperate with a multidisciplinary team of researchers and PhD candidates from the same Faculty, and researchers from the Dutch Aerospace Laboratory (NLR), dedicated to the realization of the Dutch Aviation Systems Analysis Laboratory.

Job requirements

• Familiar with challenges and benefits of sustainable aviation at aircraft and system level.
• Strong expertise with programming in MATLAB or Python.
• Strong knoweledge and expertise in conceptual and preliminary design in aviation.
• Familiar with the different sources of uncertainty and their categorization.
• Familiar with the concepts of uncertainty quantification and propagation.
• Familiar with the methods of global sensitivy analysis.
• A team-oriented mindset, eager to collaborate with researchers and industry experts.
• Excellent spoken and written English communication skills.
• Knowledge of the Dutch language is considered an advantage.

TU Delft
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 Aerospace Engineering
The Faculty of Aerospace Engineering at Delft University of Technology is a leading international community where innovation in aerospace meets global challenges. Our support and scientific staff, including PhD candidates, postdocs, and students, largely work together on three main themes: the energy transition, sustainable aerospace, and safety and security, with the aim of tackling climate change and contributing to the independence and security of Europe.

When you join us, you become part of a diverse, collaborative, and forward-thinking environment where your ideas and perspectives are valued. Our work extends beyond the lab—into field labs, innovation hubs, and partnerships with other faculties, research institutes, governments, and industry, both locally and globally.

We are committed to fostering an inclusive and welcoming workplace, assisted by an active Diversity & Inclusion team. This includes tangible support such as funding for extra personnel for family and caregiving responsibilities, mentoring programmes, and initiatives that promote cultural exchange and integration.

You don’t just join our faculty — you join a community where you can thrive, grow, and help shape the future of aerospace.

Click here to go to the website of the Faculty of Aerospace Engineering.