Delft University of Technology (TU Delft), Netherlands invites online Application for number of Fully Funded PhD Degree at various Departments. We are providing a list of Fully Funded PhD Programs available at Delft University of Technology (TU Delft), Netherlands.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Hybrid Safe Learning for Inter-Connected Systems
Online learning algorithms achieve robustness often at the expense of performance, as they are very cautious by design. This, in turn, makes them less practical for problems where speed is of utmost priority. On the other hand, offline learning, such as Deep Learning, often suffers from distribution shifts, lack of training data, and poor adaptability to unseen conditions and new problems. Can we combine these two fundamental learning paradigms to synthesize new learning tools that are both fast and adaptive?
This thesis aims to develop a robust hybrid learning framework that lies at the nexus of online and offline learning. The developed algorithms should be able to benefit from training data, when these are available, and also to learn from real-time, potentially non-IID, streaming data; should be able to track the evolution of key features and achieve model plasticity while avoiding catastrophic forgetting; and should come with interpretable and robust accuracy (generally, performance) guarantees. The designed algorithms will be applied to key problems in the domain of safe learning for interconnected systems (e.g., 6G and Edge AI platforms, self-driving vehicle vision) in collaboration with industry partners and domain experts.
This PhD thesis is offered in the context of the Marie Curie Doctoral Networks “FINALITY”, will be hosted at TU Delft, Department of Computer Science, and will be co-supervised by Prof. George Iosifidis (TU Delft) and Prof. Constantine Dovrolis (University of Cyprus, and Cyprus Institute).
Deadline : 31 May 2026
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Microbial Transformation Processes in Contaminated Sediment Depots
Contaminated sediment depots contain mixtures of polluted dredged sediments that have long term evolved under anaerobic and often brackish environmental conditions. While these depots are extensive monitored, little is known about the changes in microbial communities and pollutants over decades of in-situ storage. The system is strongly stratified, poorly mixed, and exhibits sharp gradients in geochemistry and microbial activity, resulting in major uncertainties in pollutant fate, microbial processes, and long term environmental impacts
A central knowledge gap concerns whether indigenous anaerobic microbial communities can transform persistent pollutants. Identifying degradation pathways, responsible enzymes, and the environmental conditions that drive these transformations is essential to support risk assessment, future remediation technologies, and the potential development of circular resource concepts.
This PhD project focuses on microbial process discovery, using advanced omics based tools to identify organisms, enzymes, and metabolic pathways capable of transforming selected pollutants under anaerobic conditions.
You will:
- collect and characterize sediment samples from the depot
- perform metagenomics, metaproteomics, and non targeted mass spectrometry analyses
- identify active transformation pathways and link enzymes to organisms
- perform enrichment cultivation under controlled redox conditions
- develop proof of concept bioreactor experiments to demonstrate microbial degradation of selected contaminates
Deadline : 25 May 2026
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Quantum‑Enhanced Vibration-Based Damage Detection
As a PhD candidate at TU Delft, you will investigate how advanced vibration measurements and interpretable AI models can improve the early detection of structural damage in composite materials. Your research will focus on combining high-precision Quantum Photonic Vibrometry with machine learning techniques capable of identifying subtle changes in structural behaviour.
You will design and conduct vibration experiments using QPV sensing systems and analyse complex measurement data to identify signatures of micro-scale damage. In parallel, you will develop advanced and interpretable AI models for damage detection and condition monitoring, with a strong focus on robustness, explainability, and practical applicability.
Part of the project involves deploying lightweight AI models on reservoir computing devices and edge AI hardware, enabling efficient and scalable real-time monitoring solutions. You will collaborate closely with academic researchers and industrial partners, contributing to both fundamental research and application-driven innovation.
You will become part of a supportive and international research team within TU Delft, with access to state-of-the-art sensing facilities, advanced computational resources, and collaborative industrial environments. The position offers the opportunity to work at the intersection of quantum sensing, artificial intelligence, and structural integrity for aerospace and renewable energy applications.
Deadline : 28 Jun 2026
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position – Semiconductor Materials for Quantum Technology
We are seeking for a motivated PhD candidate to join us at the Scappucci Lab and help us contribute to the development of next-generation semiconductor materials for quantum technology. Our team has pioneered advanced semiconductor materials for spin-based quantum computing, and with this project we aim to take a bold step forward.
As a PhD researcher, you will design and validate new germanium and silicon-based material stacks and devices to overcome current limitations of semiconductor qubits. By enabling qubits that are more uniform and more coherent your work will help tackle key challenges on the path toward scalable and useful quantum computers.
Throughout your PhD, you will develop a broad and valuable skill set, including:
- Semiconductor material design and advanced epitaxial deposition.
- Nanofabrication of quantum devices.
- Low-temperature electrical characterization of semiconductor quantum devices and circuits.
You will also have the opportunity to work in a collaborative, international, and interdisciplinary environment, where different perspectives and backgrounds are valued as drivers of scientific innovation.
Deadline : 2 Jun 2026
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Scientific Machine Learning, Toward Scientific Foundation Models
We invite applications for a fully funded PhD position in the area of Scientific Machine Learning (SciML), which integrates data-driven machine learning techniques with established scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods, such as physics-informed neural networks, neural operators (e.g., Fourier Neural Operators) and hybrid physics-ML approaches.
These models are expected to play a significant role in scientific domains and critical applications such as climate and geoscience, as well as the energy sector (for example, subsurface modeling, seismic inversion, climate prediction, renewable energy forecasting, and power grid optimization).
Building on this, the project focuses on the definition, development, and analysis of scientific foundation models: large-scale, generalizable models trained across diverse scientific datasets that aim to capture the underlying principles of physical systems and can be adapted to a wide range of tasks. Within this broad theme, the PhD project can take several possible directions. One direction is to develop scientific foundation models for inverse problems, moving beyond forward simulation toward tasks such as inferring hidden physical parameters, reconstructing unknown states, or identifying governing mechanisms from indirect or partial observations. Other possible directions include developing uncertainty-aware methods that can identify unreliable predictions and indicate where additional data would be most valuable; studying how such foundation models generalize across related but distinct physical settings, such as changes in boundary conditions, geometries, parameters, or forcing terms; and exploring their potential to accelerate or complement conventional numerical simulations.
Deadline : 14 Jun 2026
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(06) PhD Degree – Fully Funded
PhD position summary/title: 2 PhD Positions in Quantum Network Systems
Two PhD positions in the domain of quantum network systems are available in the group of Prof. Dr. Stephanie Wehner at QuTech, Delft University of Technology. Both positions are embedded in the Quantum Internet Alliance collaboration.
1. Quantum Node Operating Systems
The goal of this PhD position is to advance QNodeOS, the node operating system that allows the execution of quantum network applications on user devices (end nodes) connected to a quantum network.
This PhD is part of the highly collaborative effort of the Quantum Internet Alliance (QIA), and targets optimization and adaption of QNodeOS to allow deployment on QIA’s realworld quantum network prototype. You will work within QIA’s large, multidisciplinary collaboration and are expected to take ownership of a clearly defined technical role in this realization of the prototype network, which may become the first of its kind in the world.
The successful candidate is strongly motivated by designing and developing real-world software systems for quantum networks at the forefront of R&D, and is excited by handson research that brings quantum networking technology out of the lab and into operational prototypes. This is a systems PhD in the domain of experimental quantum computer science, including the realization and demonstration of future versions of QNodeOS on real quantum networking hardware.
2. Quantum Network Control Plane
A control plane architecture (Aqron) was developed for a quantum internet, that is, a general purpose quantum network capable of creating entanglement between end nodes. Simultaneously, control plan architectures exist for near term QKD networks, where only keys can be distributed between end nodes. The goal of this PhD is to device a unified control plane architecture that can deal with both modalities.
This PhD is part of the highly collaborative effort of the Quantum Internet Alliance (QIA) in its collaboration with near term QKD efforts, and has the potential for lasting impact in the deployment and development of quantum networks.
The successful candidate is strongly motivated by designing and developing real-world software systems for quantum networks at the forefront of R&D, and is excited by handson research that brings quantum networking technology out of the lab and into operational prototypes. This is a systems PhD in the domain of experimental quantum computer science, including the realization of control plane demonstrations.
Deadline : 7 Jun 2026
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Next-Generation Ultra-Low Power Class-D Audio Amplifiers for Portable Devices
As the demand for “always-on” wearable devices like Smart Glasses and Hearables grows, the audio system-on-chip (SoC) faces a critical paradox: how to deliver high-fidelity sound (high linearity) while operating on a microscopic power budget.
This PhD project focuses on the research and development of Advanced Class-D Audio Amplifier architectures. Your goal will be to innovate beyond standard PWM techniques, exploring novel feedback loops, multi-level switching, or hybrid mixed-signal topologies to achieve unprecedented efficiency.
Deadline : 15 Jun 2026
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Effect of Tramp Elements on hot Rolled Microstructures in Steels
The steel industry is undergoing a major transformation to meet sustainability and emissions goals. Particularly, steel production is moving into hydrogen-based DRI (Direct Reduced Iron) and EAF (Electric Arc Furnace) routes, which will significantly increase the use of scrap metal. However, scrap introduces unwanted impurities known as tramp elements—such as copper (Cu), tin (Sn), and nickel (Ni)—which can negatively impact the steel’s microstructure and mechanical properties.
This project investigates how these tramp elements affect the microstructure and performance of a high-value steel grade: a dual-phase automotive steel. The goal is to understand their impact during hot rolling, cold rolling, and annealing processes and to develop process strategies to mitigate potential negative effects. By doing so, the project supports the production of high-quality steels from recycled materials, strengthening circularity in the steel sector and contributing to EU and global CO₂ reduction targets.
The research will be conducted at TU Delft in collaboration with Tata Steel Netherlands, combining experimental work with advanced microstructural analysis and mechanical testing. The results will help guide future steel design and processing in scrap-rich manufacturing environments.
We are looking for an ambitious PhD-researcher to join this project. As a PhD-researcher, you will work in a stimulating academic environment and collaborate with a leading industrial partner. You will also have the opportunity to contribute to the academic community through discussions, the co-supervision of students, and guest lectures.
Deadline : 8 Jun 2026
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Single-Molecule Biophysic
We are looking for a PhD student passionate about understanding molecular mechanisms in biological systems. This position involves applying single-molecule techniques to study molecular interactions, including protein-ligand interactions and protein-peptide interactions. The candidate will work in an interdisciplinary environment, combining experimental approaches (single molecule fluorescence and biochemistry) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate with international partners for therapeutic applications of this research. The project offers opportunities to make significant contributions to understanding molecular interactions and their implications for both fundamental and medical sciences.
Deadline : 29 Jun 2026
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Rethinking Electrical Engineering Education in the Age of AI
AI is changing what it means to be an electrical engineer — and we think that’s an exciting research questions out there right now. At TU Delft’s Electrical Engineering Education (EEE) section, we’re looking for a PhD researcher to dig into something that really matters: as AI tools become capable of coding, designing circuits, and processing signals, what should students actually learn, and how do we know when they’ve learned it?
This isn’t just a theoretical exercise. AI is already reshaping engineering practice at speed — and that raises urgent questions about which competencies remain essential for critical judgement, systems thinking, and responsible decision-making, and which can be meaningfully supported by AI. Your research will help us figure out where that line is, and what it means for how we teach.
As part of our team, you’ll take a fresh look at EE curricula — not just what students learn, but how they learn and how we assess their growth. A central aim of the project is to move beyond grading final outputs and start understanding what’s happening in between: the reasoning, the decision-making, the creative instincts students bring when working with AI. We want to know how students think, not just what they produce.
The research is grounded in a critical thinking perspective and explores how AI shifts the balance between theory, practice, and the genuinely creative parts of engineering — design, system development, problem framing. You’ll be working in our hands-on educational environments at TU Delft: labs, makerspaces, project-based courses, and real electrical engineering classrooms. You’ll investigate how students interact with AI tools, how that shapes their learning behaviours and sense of agency, and how they position their own contributions relative to what the AI generates.
Methodologically, you’ll combine design-based research, learning analytics, and qualitative methods to develop and test new approaches to learning and assessment — formats that make students’ reasoning visible, that encourage critical engagement with AI-generated solutions, and that support reflective, responsible use of AI.
In practice, this means you’ll be investigating AI use in EE learning environments, examining its impact on what competencies matter, designing and running new assessment approaches, and developing ways to evaluate students’ reasoning beyond the final answer. You’ll work closely with teaching staff and contribute to academic publications throughout.
What you’ll help us build: new insights into how AI reshapes competencies and student agency in EE education, a clearer picture of which skills are essential versus AI-supported, and practical frameworks that align learning objectives, AI use, and assessment. Your work will shape the future of EE education at TU Delft — and contribute to leading venues in engineering education and the learning sciences.
Deadline : 14 Jun 2026
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on Life Cycle Assessment for Design of Structures
This position is part of the Dutch Aviation Systems Analysis Lab (DASAL) project, a collaboration between TU Delft and Royal NLR within the Dutch Luchtvaart in Transitie programme. DASAL develops computational and simulation models to assess the effects of aviation innovations and policy choices on sustainability, economic, and societal impact.
The life-cycle impact of conventional and commercial aircraft is largely driven by their fuel efficiency, given the large number of flight hours these vehicles accumulate. From a structural design perspective, this efficiency primarily translates into lightweighting, driving most current research toward lighter structures. However, the energy transition in aviation poses new scenarios that may affect the aforementioned relationship between life-cycle impact and fuel efficiency. For instance, the use of green hydrogen as a low-emission fuel might bring to the forefront other life-cycle aspects beyond fuel efficiency, potentially introducing significant changes in the design principles for novel structures. Additionally, hybrid and electric aircraft will require unprecedented levels of maintenance related to battery exchange, which will also drive structural designs towards easier-to-maintain options.
The LCA community uses life-cycle analysis as a decision-making method rather than a quantitative objective function, yet designs are usually defined by quantitative indicators, such as the mass of the system, costs to acquire and process raw materials, costs to manufacture the designed parts, and so forth. Therefore, this PhD research will have the challenge of defining structural design-compatible LCA methods that are accurate enough to produce the minimum level of certainty compatible with conceptual and preliminary design phases, and enable a good trade-off.
Furthermore, to answer some of the research questions, the PhD researcher might need to define experiments aimed at refining some of the LCA assumptions or data that will be considered critical during the research.
Deadline : 1 Jul 2026
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Probabilistic Inference in Complex Networks
Complex systems are often modeled with networks, structures where the main units of information are pairs or groups of nodes connected by edges. As network data becomes increasingly available across scientific domains, there is a need for principled models to analyze this specific structured data. How can we perform inference tasks to learn hidden patterns, like community structure or hidden hierarchies? How can we incorporate domain knowledge to design interpretable models for their governing mechanisms? How can we make these model computationally efficient and capable of scaling to large dataset sizes?
Scientific challenges for this exciting PhD project include: develop principled probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models.
Deadline : 31 May 2026
(13) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Theoretical Biophysics
During the development of an organism, gene regulation allows proteins to be expressed so that different cells can fulfil different functions. Traditionally, it has been assumed that the concentration of a particular transcription factor protein presents a signal that regulates gene expression downstream. However, in early development, it is often the duration of a signal that regulates gene expression. In this project, we will explore how organisms can extract information from the duration of a signal theoretically, in tight collaboration with experimental colleagues. The project requires interest in developing statistical physics, stochastic processes, or information methods, a focus on numerics and analytics, an interest in modeling, and a genuine interest in engaging in the biological system and working closely with our collaborators
Deadline : 20 May 2026
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Modeling and Managing Future Low-Emission Aircraft in European Airspace
We are seeking a dedicated PhD candidate to join a new project, ZEUS, developing integrated frameworks for modeling, assessment, and management of next-generation low/zero-emission aircraft impact on the European ATM network.
Your responsibilities will include developing performance models for zero-emission aircraft (hydrogen combustion, hydrogen fuel cell, battery-electric, hybrid-electric) and assessing ATM network impacts of aircraft with fundamentally different operational characteristics. You will simulate mixed-fleet operations in European airspace and analyze how they will impact the future air traffic system, as well as explore new ways of managing these new entrants. The role involves investigating trajectory planning and energy management strategies for alternative propulsion, modeling differences in climb/descent performance, range, and endurance constraints, and evaluating airspace capacity implications and traffic flow management adaptations.
Deadline : 31 May 2026
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Cybersecurity and Post-Quantum Cryptography for Aviation Systems
We are seeking an exceptional PhD candidate to join the PostQat project, addressing the emerging cryptographic challenge in aviation by exploring secure architectures, protocols, and implementation strategies for future air traffic management (ATM) communication systems.
Your responsibilities will include conducting security assessments of aviation communication, navigation, and surveillance (CNS) systems, and analyzing quantum threats to ADS-B, CPDLC, ACARS, VHF communications, and satellite systems. You will design protocols for quantum-resistant cryptography adapted for aviation applications. The research will also define service and functional requirements for post-quantum secure air traffic management infrastructure.
The role will also involve addressing resource constraints of embedded avionics hardware and long-lifecycle aviation equipment with other project partners, while developing open-source reference implementations and protocol blueprints. You will collaborate with cybersecurity experts, aviation authorities, and industry partners, contributing to the European PQC transition roadmap for aviation.
Deadline : 31 May 2026
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD Designing Digital Trust for Socio-Technical Innovation
What do multi-actor digital innovation challenges in sectors like public services, healthcare and financial services have in common? Scaling digital innovations demands shared digital trust. Digital trust is an growing interdisciplinary field looking to scale innovations in a highly insecure digital world. While the digital world expands, the mechanisms to verify who we are and what we are allowed to do remain fragmented. As part of the NWO-funded project ‘Sum Volo Satis Facio’, TU Delft is seeking a PhD candidate to work at the frontiers of digital trust. As part of a team, you will help bridge the gap between high-level regulations (like eIDAS) and the practical, technical architectures required to make digital interactions secure and scalable.
You will be part of a multi-helix innovation coalition of 14 public, private and academic partners, including Digicampus. Together, these partners provide the socio-technical components (data registries, wallets, cryptographic infrastructure) and interdisciplinary knowledge (socio-technical and innovation methods) needed to deliver breakthroughs in the adoption of digital wallets and attestations. Your research project will focus on closing the verification blind spot: how should relying parties verify digital attestations?
Deadline : 24 May 2026
(17) PhD Degree – Fully Funded
PhD position summary/title: PhD Position GreenCare4Health. Green Interventions for Heat-Resilience: Design and Evaluation
Are you passionate about using urban design to address the pressing health challenges posed by climate change? In this PhD project, you will be a key researcher in the international project GreenCare4Health, working at the intersection of urbanism, microclimate, and public health.Your primary focus will be on co-designing and evaluating green interventions to reduce heat-related health risks in dense, socially vulnerable urban areas. You will develop and test a Health-Heat (HH) toolkit, an instrument combining spatial design guidelines, participatory methods, and evaluation metrics to support school communities in re-naturalizing their yards and maintaining these spaces as “green commons”. The toolkit will be tested and refined through Urban Living Labs in close collaboration with a multidisciplinary team.
A central part of your role involves conducting multidimensional evaluations of these interventions. This includes assessing microclimate performance (e.g., temperature reduction, shading), health and wellbeing outcomes, and patterns of use and stewardship. You will work closely with researchers collecting health and climate data in pilot schools, ensuring that design solutions are evidence-based, context-sensitive, and impactful.
Deadline : 29 May 2026
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Optical Coherence Tomography for Skin Diagnostics
Skin cancer is one of the most common forms of cancer and its incidence is expected to rise rapidly in the near future. Hence, there is an urgent need for new low cost and high performance screening and diagnostics devices to meet the expected clinical demand. Are you inspired by this challenge and excited to work on the boundary between biomedicine and technology?
In this project you will develop advanced optical coherence tomography (OCT) systems based on novel near infrared supercontinuum (SC) light sources and detectors. The scientific challenge is threefold. First, the OCT system must be optimized in terms of noise and detection efficiency. You will do this based on experiments and physical modeling of SC, detector, and system performance. Second, the OCT system must obtain high-resolution deep-tissue images from clinically relevant samples. You will realize this by applying superresolution and 3D Fourier OCT signal processing techniques. Third, the OCT system must be compact, robust, and cost effective for it to be used it in a clinically relevant setting. In this PhD you work towards making an impact in skin cancer imaging in a collaboration between The Hague University of Applied Sciences and Erasmus Medical Center. As a PhD you are expected to contribute to the team and work closely together with applied physicists, clinicians, and technicians.
Deadline : 1 Jun 2026
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD Business Models for Digital Trust
What are acceptable business models for digital trust platforms? Digital identity is essential for the digital economy. While technologies and regulations for digital identity are now emerging, business models are still missing. Those business models should provide fair compensations to identity providers. They need to be scalable, because only then the digital identity solutions create impact. But we should avoid business models that create big tech-alike monopolies or have perverse incentives that create huge costs.
As part of the NWO-funded project ‘Sum Volo Satis Facio’, TU Delft is seeking a PhD candidate to work at the frontiers of digital trust. This position has the specific focus on business models for digital identity platforms. As part of a team, you will help bridge the gap between high-level regulations (like eIDAS) and the practical, technical architectures required to make digital interactions secure and scalable. You will be part of a multi-helix innovation coalition of 14 public, private and academic partners, including Digicampus. Together, these partners provide the socio-technical components (data registries, wallets, cryptographic infrastructure) and interdisciplinary knowledge (socio-technical and innovation methods) needed to deliver breakthroughs in the adoption of digital wallets and attestations. Your research will be pioneering work on the cross-roads of business models and digital identity. You will examine fair and acceptable business models for platforms that provide identity attestations. Such identity information is essential for a digital economy that actors can trust in. Fair compensation is needed such that identity platforms become available in the market, but without perverse incentives that lead to high societal costs. You will be working with leading academics, domain experts, engineers and digital trust professionals.
This PhD position is aimed at candidates with a Master’s degree in business, information systems, data science, computer science, or related fields, with a strong interest in the topic of digital trust or digital infrastructures. The successful candidate will be based at the Department of Engineering Systems and Services (ESS) at the Delft University of Technology under the supervision of Prof.dr.ir. Nitesh Bharosa and Prof.dr.ir. Mark de Reuver.
Deadline : 24 May 2026
(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Physics-informed Foundation Models for Robotics
In big tech, a race is underway to collect as much robotic data as possible, with the goal of training the ChatGPT of robotics physical intelligence. But physical data are much harder to get than language examples!
We are looking for a PhD candidate to join the Physical Intelligence Lab at TU Delft and contribute to the development of physics-informed foundation models for robotic manipulation. The position is part of the ambitious European project GRAIL, which aims to extend the paradigm of foundation models to systems interacting with the physical world; let’s build the first European foundation model for robotics together!
The PhD candidate will help solving the robotic data grap by developing learning architectures that integrate physical structure from mechanics and dynamical systems into modern machine learning frameworks. This way, the models will not have to learn physics from data everytime and will be able to focus only on what is actually new. The work will involve developing representations that enable generalization across tasks, environments, and robotic platforms, with particular attention to deformable media and compliant manipulation.
The candidate will be supervised by Dr. Della Santina and work within a collaborative research environment spanning control theory, machine learning, and (soft) robotics, with access to experimental platforms and collaborations across the European consortium. The consortium will include AI&Robotics companies and world experts in deep learning from all over Europe.
Deadline : Open until filled
About Delft University of Technology (TU Delft), Netherlands –Official Website
Delft University of Technology, also known as TU Delft, is the oldest and largest Dutch public technical university. Located in Delft, Netherlands, it is consistently ranked as one of the best universities in the Netherlands, and as of 2020 it is ranked by QS World University Rankings among the top 15 engineering and technology universities in the world.
With eight faculties and numerous research institutes, it has more than 26,000 students (undergraduate and postgraduate) and 6,000 employees (teaching, research, support and management staff).
The university was established on 8 January 1842 by William II of the Netherlands as a Royal Academy, with the primary purpose of training civil servants for work in the Dutch East Indies. The school expanded its research and education curriculum over time, becoming a polytechnic school in 1864 and an institute of technology (making it a full-fledged university) in 1905. It changed its name to Delft University of Technology in 1986.
Dutch Nobel laureates Jacobus Henricus van ‘t Hoff, Heike Kamerlingh Onnes, and Simon van der Meer have been associated with TU Delft. TU Delft is a member of several university federations, including the IDEA League, CESAER, UNITECH International and 4TU.
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