PhD Position Multimodal modeling of Trust Development for Trust Calibration in AI

Job description
Hybrid Intelligence (HI) is the combination of human and artificial intelligence (AI), augmenting human intellect instead of replacing it, and developing AI which works with and for humans.

Hybrid Intelligent AI systems should actively calibrate the trust they elicit from humans they interact with by actively identifying cases of over/under-trust, and then acting to address these. To do this, they require an understanding of their human partners’ trust perceptions and how these dynamically evolve. Currently, trust perceptions are typically measured using explicit assessments, such as questionnaires at static moment in time. However, such measures lack efficiency in providing feedback for trust calibration in human-AI interactions, and effectiveness because they struggle to capture fine-grained information about how human trust perceptions dynamically evolve.

In this project, you will be working to address this issue by developing approaches using multimodal sensor data for the implicit assessment of human trust perceptions. In particular, you will work on answering the following research question: How can we enable artificial intelligence systems to efficiently and effectively assess the dynamic development of humans’ trust evaluations during collaboration for improved calibration behavior?

Given the recent advances in multimodal language modeling (e.g., MLLMs), this project will explore approaches that leverage unstructured language data describing elements of human trust perceptions and associated reasoning processes during interactions captured with a “Think Aloud” (TA) protocol. The project will involve collecting multimodal data about trust behavior; identifying data collection approaches for effective and efficient modeling of trust perceptions and calibration; and addressing a cycle of trust behavior, from understanding human trust, to adjusting agent behavior, which in turn influences human trust.

This project will be supervised by dr. Bernd Dudzik and dr. Myrthe Tielman. That means the candidate will be a part of both the research group of Pattern Recognition and Interactive Intelligence, both within the Intelligent Systems section of Computer Science, EEMCS. Additional co-supervisors will be Prof. Dr. Mark Neerincx (II, TU Delft & TNO) and Prof. Dr. Dan Balliet (VU Amsterdam).

This project is part of a larger research effort within the Hybrid Intelligence Center, where you will have the opportunity to collaborate with researchers across multiple universities and disciplines.

Job requirements
We are looking for a candidate who meets the following essential criteria:

• A Master’s degree or equivalent (or about to graduate with one) in a relevant field (Artificial Intelligence, Computer Science, Data Science, Cognitive Science, etc.)
• A good command of spoken & written English
• A curiosity-driven mindset and a willingness to learn
• Some experience with ML or NLP
• Some experience in collecting multimodal datasets or running experiments with human subjects

We encourage you to apply even if you do not meet all the criteria above as long as you are willing to acquire the relevant skills.

Additionally, the following criteria are appreciated:

• Experience with interdisciplinary research projects.
• Strong analytical and conceptual modelling competencies
• Good programming skills (preferably Python), including ML methods and libraries.

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 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.

Postdoc Data-Driven Cardiovascular System Dynamics Modeling for Heart Failure Populations

Job description
Heart failure remains one of the most pressing challenges in cardiovascular medicine. In particular, heart failure with preserved ejection fraction (HFpEF) or mildly reduced ejection fraction (HFmrEF) affects millions of patients and remains difficult to treat due to its complex, heterogeneous, and often misunderstood mechanisms.

At Delft University of Technology, we are launching a translational research project to address this challenge by building next-generation cardiovascular digital twins, personalized computational models of the heart and circulation, that can guide treatment decisions. Our focus is on understanding the complex interplay between cardiac function and systemic hemodynamics, both at rest and during exercise. We aim to use this insight to optimize and personalize novel mechanism targeted therapies, including left to right atrial shunting, to reduce left atrial pressures, improve exercise tolerance, and potentially reverse disease progression.

As a postdoctoral researcher, you will take the lead in developing and applying reduced order models of the cardiovascular system, integrating multiscale heart and circulation interactions. These models will be personalized through inverse modeling pipelines using clinical data collected during ongoing trials, including pressure and flow measurements and echocardiographic assessments. You will evaluate the mechanistic effects of interventions such as atrial shunting across virtual patient populations and contribute to the design of personalized strategies that are tuned to individual physiology.

You will work closely with leading clinical and industrial partners and will play a key role in translating computational insights into tangible scientific and therapeutic impact. This is a unique opportunity to work at the intersection of fundamental modeling, translational medicine, and real world application.

You will be part of the lab of dr. ir. M. Peirlinck in the Department of BioMechanical Engineering at TU Delft. Our group brings together experimental data, physics based modeling, and machine learning to understand and predict the behavior of the cardiovascular system across scales.

In this role, you will:

• Develop and implement cardiovascular digital twins based on lumped parameter models.
• Personalize these models using clinical data at rest and during exercise.
• Analyze the mechanistic impact of atrial shunting and other interventions.
• Contribute to collaborative work with clinical and industrial stakeholders.
• Publish in high quality clinical and engineering journals and present at scientific conferences.
• Support mentoring, teaching, and additional funding acquistion activities.

This position is offered for two years, with an initial one year appointment and a strong intention to extend based on performance and shared enthusiasm. We are looking for eager, motivated scientists who want to grow their careers, drive an interdisciplinary project forward, and make a direct scientific impact on the future of heart failure treatment.

Job requirements
You are an ambitious and independent researcher with a strong interest in cardiovascular modeling and a desire to make scientific contributions that translate into real-world impact. You enjoy working on complex problems at the intersection of engineering, medicine, and computation, and you are motivated to collaborate with clinical and industrial partners.

Required qualifications:

• A PhD or equivalent academic research experience in mechanical engineering, biomedical engineering, medical physics, applied mathematics, computer science, or a related field.
• A solid understanding of cardiovascular system dynamics and physiological modeling.
• Proven experience with mathematical modeling of biological or physical systems.
• Strong programming skills in Python, C++, Fortran, or a similar scientific computing language.
• Fluency in English and excellent communication skills.
• A collaborative mindset and the ability to work across disciplines.

Preferred qualifications:

• Experience with reduced order modeling of the cardiovascular circulation, particularly using zero-dimensional lumped parameter models.
• Familiarity with inverse modeling, system identification, and data assimilation.
• Experience with uncertainty quantification or Bayesian inference methods for model calibration, prediction, or personalization.
• Experience working with clinical data.
• Interest in contributing to team science, mentoring, science communication, or the development of future project proposals.

Please clearly describe how your background matches the required and preferred qualifications in your motivation letter. Applications that do not address these criteria may not be considered.

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 Multimodal modeling of Trust Development for Trust Calibration in AI

Job description
Hybrid Intelligence (HI) is the combination of human and artificial intelligence (AI), augmenting human intellect instead of replacing it, and developing AI which works with and for humans.

Hybrid Intelligent AI systems should actively calibrate the trust they elicit from humans they interact with by actively identifying cases of over/under-trust, and then acting to address these. To do this, they require an understanding of their human partners’ trust perceptions and how these dynamically evolve. Currently, trust perceptions are typically measured using explicit assessments, such as questionnaires at static moment in time. However, such measures lack efficiency in providing feedback for trust calibration in human-AI interactions, and effectiveness because they struggle to capture fine-grained information about how human trust perceptions dynamically evolve.

In this project, you will be working to address this issue by developing approaches using multimodal sensor data for the implicit assessment of human trust perceptions. In particular, you will work on answering the following research question: How can we enable artificial intelligence systems to efficiently and effectively assess the dynamic development of humans’ trust evaluations during collaboration for improved calibration behavior?

Given the recent advances in multimodal language modeling (e.g., MLLMs), this project will explore approaches that leverage unstructured language data describing elements of human trust perceptions and associated reasoning processes during interactions captured with a “Think Aloud” (TA) protocol. The project will involve collecting multimodal data about trust behavior; identifying data collection approaches for effective and efficient modeling of trust perceptions and calibration; and addressing a cycle of trust behavior, from understanding human trust, to adjusting agent behavior, which in turn influences human trust.

This project will be supervised by dr. Bernd Dudzik and dr. Myrthe Tielman. That means the candidate will be a part of both the research group of Pattern Recognition and Interactive Intelligence, both within the Intelligent Systems section of Computer Science, EEMCS. Additional co-supervisors will be Prof. Dr. Mark Neerincx (II, TU Delft & TNO) and Prof. Dr. Dan Balliet (VU Amsterdam).

This project is part of a larger research effort within the Hybrid Intelligence Center, where you will have the opportunity to collaborate with researchers across multiple universities and disciplines.

Job requirements
We are looking for a candidate who meets the following essential criteria:

• A Master’s degree or equivalent (or about to graduate with one) in a relevant field (Artificial Intelligence, Computer Science, Data Science, Cognitive Science, etc.)
• A good command of spoken & written English
• A curiosity-driven mindset and a willingness to learn
• Some experience with ML or NLP
• Some experience in collecting multimodal datasets or running experiments with human subjects

We encourage you to apply even if you do not meet all the criteria above as long as you are willing to acquire the relevant skills.

Additionally, the following criteria are appreciated:

• Experience with interdisciplinary research projects.
• Strong analytical and conceptual modelling competencies
• Good programming skills (preferably Python), including ML methods and libraries.

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 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.

Postdoc Data-Driven Cardiovascular System Dynamics Modeling for Heart Failure Populations

Job description
Heart failure remains one of the most pressing challenges in cardiovascular medicine. In particular, heart failure with preserved ejection fraction (HFpEF) or mildly reduced ejection fraction (HFmrEF) affects millions of patients and remains difficult to treat due to its complex, heterogeneous, and often misunderstood mechanisms.

At Delft University of Technology, we are launching a translational research project to address this challenge by building next-generation cardiovascular digital twins, personalized computational models of the heart and circulation, that can guide treatment decisions. Our focus is on understanding the complex interplay between cardiac function and systemic hemodynamics, both at rest and during exercise. We aim to use this insight to optimize and personalize novel mechanism targeted therapies, including left to right atrial shunting, to reduce left atrial pressures, improve exercise tolerance, and potentially reverse disease progression.

As a postdoctoral researcher, you will take the lead in developing and applying reduced order models of the cardiovascular system, integrating multiscale heart and circulation interactions. These models will be personalized through inverse modeling pipelines using clinical data collected during ongoing trials, including pressure and flow measurements and echocardiographic assessments. You will evaluate the mechanistic effects of interventions such as atrial shunting across virtual patient populations and contribute to the design of personalized strategies that are tuned to individual physiology.

You will work closely with leading clinical and industrial partners and will play a key role in translating computational insights into tangible scientific and therapeutic impact. This is a unique opportunity to work at the intersection of fundamental modeling, translational medicine, and real world application.

You will be part of the lab of dr. ir. M. Peirlinck in the Department of BioMechanical Engineering at TU Delft. Our group brings together experimental data, physics based modeling, and machine learning to understand and predict the behavior of the cardiovascular system across scales.

In this role, you will:

• Develop and implement cardiovascular digital twins based on lumped parameter models.
• Personalize these models using clinical data at rest and during exercise.
• Analyze the mechanistic impact of atrial shunting and other interventions.
• Contribute to collaborative work with clinical and industrial stakeholders.
• Publish in high quality clinical and engineering journals and present at scientific conferences.
• Support mentoring, teaching, and additional funding acquistion activities.

This position is offered for two years, with an initial one year appointment and a strong intention to extend based on performance and shared enthusiasm. We are looking for eager, motivated scientists who want to grow their careers, drive an interdisciplinary project forward, and make a direct scientific impact on the future of heart failure treatment.

Job requirements
You are an ambitious and independent researcher with a strong interest in cardiovascular modeling and a desire to make scientific contributions that translate into real-world impact. You enjoy working on complex problems at the intersection of engineering, medicine, and computation, and you are motivated to collaborate with clinical and industrial partners.

Required qualifications:

• A PhD or equivalent academic research experience in mechanical engineering, biomedical engineering, medical physics, applied mathematics, computer science, or a related field.
• A solid understanding of cardiovascular system dynamics and physiological modeling.
• Proven experience with mathematical modeling of biological or physical systems.
• Strong programming skills in Python, C++, Fortran, or a similar scientific computing language.
• Fluency in English and excellent communication skills.
• A collaborative mindset and the ability to work across disciplines.

Preferred qualifications:

• Experience with reduced order modeling of the cardiovascular circulation, particularly using zero-dimensional lumped parameter models.
• Familiarity with inverse modeling, system identification, and data assimilation.
• Experience with uncertainty quantification or Bayesian inference methods for model calibration, prediction, or personalization.
• Experience working with clinical data.
• Interest in contributing to team science, mentoring, science communication, or the development of future project proposals.

Please clearly describe how your background matches the required and preferred qualifications in your motivation letter. Applications that do not address these criteria may not be considered.

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.

PostDoc Survivable DC Power Systems for Ships

Job description
Energy transition, smart manning, and survivability are three of the main challenges of the Navy and the rest of the maritime sector. The NWO project "Survivable DC Power Systems for Ships" investigates DC system technology that makes it possible to continue operation after failures from wear, calamities such as fires and floods, or missile impact, by being fault tolerant. This project aims to answer the following research questions. First, how to design DC system architectures, components, and protection for vessels in such a way that survivability is maximized? Secondly, how can reliability of the DC system and its components be modelled in such a way that performance can be guaranteed? Thirdly, how to design and control fault tolerant decentralized DC energy systems, and how to integrate them into the ship design? Fourthly, is it viable to replace a part of the DC system conductors by superconductors; what are the benefits of these superconductors and in which parts of the power system should thy be applied? TUDelft, TUEindhoven and UTwente are collaborating in this project.

This vacancy focusses on the third research question: How to guarantee the availability of power and energy on board of a chip with a DC electric power system? How to design and control a fault tolerant energy system both in normal operation and in the case of extreme events? How can energy sources and loads be distributed over the vessel to ensure safety, reliability, availability, and efficiency? And what are the implications of this DC system architecture for the ship design? Extreme events include electromagnetic guns or laser weapons requiring extremely high power for a very short time and consequences of missile impacts, such of compartment flooding or fire.

The research is carried out in close cooperation with national academic and industrial partners working on the NWO-funded project Survivable DC Power Systems for Ships. The industrial partners are providing access to use cases and recorded data of state-of-the art ships. The zero-emission laboratory of MARIN can be used for validation of the control strategies with real components.

The candidate will be part of a team of PhD students working on different aspects of energy transition in the maritime sector, including combustion engines on e-fuels (hydrogen, methanol, ammonia), fuel cells, DC systems, and sizing and control of the power, proplusion and energy system.

The candidate will work at the Delft University of Technology, in the Department of Maritime & Transport Technology, in the Group on Sustainable Drive and Energy Systems under the supervision and guidance of experts in electrical and marine engineering, Dr. Henk Polinder and Dr. Andrea Coraddu. Keyword: shipdrivemenens.

Job requirements
We are looking for an outstanding and enthusiastic PostDoc candidate who has expertise and/or interest in modelling and design of electric power and propulsion systems. You have obtained a PhD degree or expect to obtain a PhD very soon related to these areas: electrical power engineering, marine engineering, control engineering.

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.