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 Positions Energy-efficient Computation-In-Memory applications
For edge-AI applications like personalized healthcare, computing systems with unprecedented energy efficiency are essential. Novel computing paradigms, such as computation-in-memory (CIM) using conventional SRAM and memristive devices offer significant energy efficiency potential. However, CIM requires significant research and development efforts in different aspects related to the full stack of computing systems design, e.g., circuits, micro-architectures, system architectures, compilers, and algorithms, along with design tools and methodologies. The Computer Engineering (CE) section of the Department of Quantum & Computer Engineering (QCE) is looking for motivated candidates inspired to work on novel neuromorphic algorithms, system architecture and circuit design for CIM. The role is also to demonstrate CIM adaptability towards changes in neuromorphic models and training methods as well as its suitability for edge applications. As a result, the CE group have the following open PhD positions:
Deadline :September 30, 2023
(02) PhD Degree – Fully Funded
PhD position summary/title: PhD Synthetic cell division
Living cells are highly complex systems made of countless lifeless molecular components. We do not understand how these interact to form a living cell that sustains itself, grows and divides. The BaSyC initiative is a collaborative 10-year research program that aims to understand ‘how life works’ by building an autonomous self-reproducing synthetic cell from the bottom up. As a container, we use giant unilamellar vesicles, which are cell-sized, lipid bilayer-enclosed reaction compartments that can be visualized by real-time microscopy and directly manipulated using biophysical tools. The Koenderink and Dekker labs study the process of cell division, which requires constriction of the cell into a dumbbell-shape, followed by neck abscission to split the cell into two daughter cells. Since the mechanism of abscission is still poorly understood, it remains a big challenge in the synthetic cell field to achieve robust cell division. This experimental biophysics project aims to achieve robust synthetic cell abscission based on a thorough understanding of the required membrane remodeling process. You will reconstitute a minimal protein-based machinery for abscission, based on proteins such as the bacterial dynamin A and the ESCRT-like bacterial PspA. We anticipate that robust abscission requires these protein machineries to work in conjunction with lipid-based mechanisms that generate high spontaneous curvature at the neck. Asymmetries in head group size between the outer and inner leaflets triggered enzymatically or by photoactive lipids can for instance introduce high local curvature. Using quantitative confocal microscopy you will study how membrane binding and the abscission activity of the dynamins depend on lipid composition, whether dynamins have any intrinsic ability to generate curvature themselves, and what is the optimal neck diameter to achieve abscission. To create narrow necks, you will use optical-tweezers available in the Koenderink lab and DNA-nanotechnology tools from the Dekker lab. You will closely collaborate with other groups within the BaSyC consortium to ensure that the abscission pathway is compatible with other functionalities of the synthetic cell. The project will furthermore be carried out in close interaction with the groups of Siewert-Jan Marrink (Groningen) and Timon Idema (Delft), who will investigate abscission with simulations and analytical calculations.
Deadline : 30-09-2023.
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(03) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Atomistic Model for Oxidation of Alloyed Steels during Annealing
Oxidation modeling is normally performed at macroscopic scales based on diffusion theories. Predictive models have been developed in the recent past to investigate the kinetics of wüstite layer formation on pure iron for a wide range of oxygen partial pressures and to probe the effect of gas composition on the kinetics of oxidation. Experimental tools (e.g., X-ray diffraction, scanning electron microscopy with energy dispersive spectroscopy, etc) can be employed to study the oxidation behavior on materials e.g., Fe and Ni-based alloys. However, most of the experimental techniques are primarily restricted to macroscopic scale and hence lack atomistic insights into interaction between oxygen atoms and alloy elements and the interfacial oxidation reaction process. To this end, Molecular dynamics (MD) can be used as a tool to study oxidation at atomistic level. MD method has been used in the existing work to explore the growth, atomic diffusion and charge state of oxidation from an atomistic perspective. However, the major limitation of the state-of-the-art MD studies is that they are primarily restricted to study oxidation process in binary alloys. This is because of the lack of reliable interatomic potentials for multi-component systems (beyond binary alloys) to be employed within MD simulations. The main aim of the project is to develop a theoretical framework for the atomistic modeling of the oxidation process on alloyed steels under annealing conditions. The atomistic model developed within this project will thus be capable of studying the effects of the steel composition and oxygen partial pressure on the growth of the oxide layer on the steel surface. Hence, the atomistic insights obtained within this project will complement the findings of the OxiTool model developed by Tata Steel together with TU Delft in an earlier project. During this four-year project, the PhD student will work within a team comprising of members from Materials Science and Engineering (MSE) department of TU Delft and Tata Steel on the above described research problem. The PhD student will especially focus on using Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate oxidation process on steels. It is expected from the applicant to have prior experience on using DFT and MD based simulation techniques. The applicant should be keen to learn new techniques for development of interatomic potentials for MD simulations.
Deadline : 31 August 2023
(04) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Development of Metasurface Components for Snapshot Spectro-polarimetry
Snapshot spectro-polarimetry aims to extract the spectral and polarization information of a scene in a single measurement. This promising technique is particularly suited for the observation of dynamic phenomena such as polar aurora for example. It is also an excellent candidate for the observation of changing scenes like the ones recorded by a satellite observing the earth atmosphere for the detection of aerosols and trace gases related to climate change. Metasurface technology is an exciting and growing field of optics with a large amount of applications. Those metasurface components are made of nanostructured material made such as anisotropic nanorods. With the appropriate design, a precise control and definition of the phase and polarization of a transmitted beam are achieved, improving the performances of various applications. For space instrumentation a metasurface component could be used to achieve both spectral and polarization separation leading to a more compact and robust instrument design.
Deadline : 31 August 2023
(05) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Digital Twin for Green Shipping
The Digital Twin for Green Shipping (DT4GS) is a Horizon Europe project with over 20 industry and university partners aimed at delivering an “Open Digital Twin Framework” for shipping companies and waterborne industry actors to tap into new opportunities made available through the use of Digital Twins (DTs). DT4GS will enable shipping stakeholders to embrace DT innovations to support smart green shipping in the upgrade of existing ships and new vessels. DT4GS will cover the full ship lifecycle. Applications will focus on shipping companies but will also provide decarbonisation decision-support system for shipyards, equipment manufacturers, port authorities and operators, river commissions, classification societies, energy companies and transport/corridor infrastructure companies
Deadline : 31 August 2023
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(06) PhD Degree – Fully Funded
PhD position summary/title: PhD Position How Sediment and Vegetation Build Coastal Dune Landscapes
The candidate will study coastal processes that govern the medium to long term development (5-50 years) of the coastal dune landscape in view of combined climate drivers and anthropogenic influences. The study will include the governing characteristics of landscape development such as sediment mobility and the development of vegetation in combination with short term environmental conditions such as winds, waves, rainfall and droughts. How can the knowledge gap between these short term driving processes and longer term response of the coastal landscape morphology and vegetation be bridged in view of developing Building with Nature strategies? The PhD candidate will use the living lab that is provided by the Zandmotor along the Delfland coast for fieldwork and monitoring (incl. developing new monitoring approaches). The Zandmotor location provides a unique building with nature approach to coastal dune landscapes and understanding its development can contribute to the scalability of Building with Nature engineering concepts. Fieldwork will be complemented by dedicated modelling studies to predict the behaviour of coastal landscapes (foreshore, beach, dunes) and understand the scalability of these concepts.
Deadline : 31 August 2023
(07) PhD Degree – Fully Funded
PhD position summary/title: PhD Positie Publiek Private Samenwerking voor Aanpak Maatschappelijke Uitdagingen in het Fysieke Domein
De uitdagingen in de gebouwde omgeving van vandaag vragen om andere onderlinge verhoudingen tussen betrokken partijen, nieuwe vormen van publiek publieke en publiek private samenwerking. Van projectmanagement naar programmamanagement. Van transactionele contracten naar partnerships. Van hiërarchische sturing naar netwerksturing. Maar hoe moet je zo’n samenwerking dan inrichten? Dat onderzoekt de leerstoel Publiek Opdrachtgeverschap in de Bouw aan de faculteit Bouwkunde van de TU Delft. Als PhD breng je in kaart wat de uitdagingen van morgen vandaag vragen van samenwerkingsvormen en onderzoekt in de praktijk welke verandering dan nodig is ten opzichte van de huidige praktijk. Vanuit het gezichtspunt van publieke partijen kijk je wat er nodig is voor de toekomst. Je betrekt daarbij ook het perspectief en de mogelijkheden van de private samenwerkingspartners. Je kijkt niet alleen naar grote, meeslepende projecten, maar juist ook het meer dagelijkse, repetitieve werk in het asset management, zoals bij beheer en onderhoud en kleine aanpassingen, datgene wat echt onze dagelijkse leefwereld bepaalt.
Deadline : 27 augustus 2023
(08) PhD Degree – Fully Funded
PhD position summary/title: PhD Positie Uitdagingen voor de Interne Organisatie van Publieke Opdrachtgevers als Samenwerkingspartner bij Maatschappelijke Opgaven
De uitdagingen in de gebouwde omgeving van vandaag vragen om andere onderlinge verhoudingen tussen betrokken partijen, nieuwe vormen van publiek publieke en publiek private samenwerking. Van projectmanagement naar programmamanagement. Van transactionele contracten naar partnerships. Van hiërarchische sturing naar netwerksturing. Maar welke organisatieinrichting past daar bij? Welke aanpassing is er nodig binnen publieke organisaties om gesteld te staan voor die nieuwe samenwerkingsvormen die horen bij grote maatschappelijke uitdagingen? Dat onderzoekt de leerstoel Publiek Opdrachtgeverschap in de Bouw aan de faculteit Bouwkunde van de TU Delft. Als PhD breng je in kaart wat de uitdagingen van morgen vandaag vragen van de publieke organisatie en onderzoekt in de praktijk welke verandering dan nodig is ten opzichte van de huidige praktijk in de sturing en governance binnen de publieke organisatie. Je kijkt niet alleen naar grote, meeslepende projecten, maar juist ook het meer dagelijkse, repetitieve werk in het asset management, zoals bij beheer en onderhoud en kleine aanpassingen, datgene wat echt onze dagelijkse leefwereld bepaalt.
Deadline : 27 augustus 2023
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(09) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Guaranteeing the Availability of Power and Energy system on Board of Ships
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 enables integration of energy from renewable sources, and 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 meshed 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.
Deadline : 20 August 2023
(10) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Reconfigurable Decision-making Methods for Sustainable Manufacturing
The manufacturing industry faces high unpredictability of market requirements, rapid technological changes and sustainability challenges. To survive and compete in this environment, manufacturers and decision-makers at different levels are pressured to develop the reconfigurability capability, that is the ability to rapidly and cost-effectively reconfigure complex systems (of systems) to changing environments. In industry and society, the development of the reconfigurability capability requires the development, industrial implementation, operation and reconfiguration of both technical solutions and decision support systems. Reconfigurable technical solutions – such as modular machines, reconfigurable robots, smart systems, and digital twins – ensure technical feasibility of reconfigurations. To this regard, literature on reconfigurable, modular, and smart manufacturing systems, has largely contributed to develop and mature these solutions.
Deadline : 20 August 2023
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(11) PhD Degree – Fully Funded
PhD position summary/title: PhD student on a project “Self-assembled microgels as rheology modifiers for formulation technologies” as part of MultiSMART
We seek an outstanding Doctoral Candidate to study Self-assembled microgels as rheology modifiers for formulation technologies. We are looking for candidates, without gender or nationality discrimination but with mobility requirement, with interest in supramolecular chemistry or physical chemistry of soft materials. Goals of the PhD project include the fabrication of hierarchically structured microgels by using a.o. microfluidic techniques, that mimic the structure and viscoelastic properties of biological tissues, and to explore the properties of these novel materials. In this challenging experimental project you will use a broad variety of spectroscopic, microscopic and material characterisation techniques, to study the physical chemistry of soft, hierarchically structured materials
Deadline : 15-8-2023
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(12) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Circular Supply Chain Management for the Renewal and Renovation of Urban Bridges and Quay Walls
Many bridges and quays in historic city centres, such as in Amsterdam, show signs of overdue maintenance and have reached the end of their technical or functional life. Renovation or renewal is required in the short term to keep cities save, accessible, functioning and liveable. This is a large, costly and complex task, not only because of its size, but also because of the crowded environment, the effects for local residents, traffic and businesses, and the desire to keep original bridges and quay walls for preservation of cultural heritage. In addition since recent years, construction works need to implement environmentally considerate, sustainable and circular approaches. This PhD research is part of the Loqiquay project that aims at accelerating bridge and quay wall projects, increasing logistics and project control, and improving sustainability through closed-loop material cycles for reuse of secondary materials. The research methodology is based on modelling simulation for joint logistics and project planning, closed-loop materials flow and stock analysis, and multi-actor adaptive control of simultaneous projects. Due to the high societal, environmental and economical stakes, it is imperative that logistical prioritization, mitigation and decision-making dynamically assess and respect the interests of involved stakeholders and the environment.
Deadline :14 August 2023
(13) PhD Degree – Fully Funded
PhD position summary/title: 2 PhD projects on flow physics of Pickering emulsions for sustainable chemical conversion
Two PhD projects are available in the Garbin lab (https://garbinlab.org) on the NWO-Vici project “Flow physics of Pickering emulsion reactors for sustainable chemical conversion”.The project aims to unravel the flow physics of multicomponent, multiphase systems with complex interfaces, which are of emerging interest in areas ranging from advanced materials, to chemical conversion, to airborne disease transmission.You will develop and integrate novel experimental approaches including in-situ, real-time visualization of concentration fields and advanced microstructure imaging, combined with multiscale modelling. You will apply this new approach to the case of Pickering emulsions for chemical conversion. These water/organic emulsions stabilized by solid particles hold exciting potential as platforms for sustainable chemical processing, promising higher conversion rates and selectivity, and easier catalyst recovery. Despite promising lab-scale findings, industry-scale application of Pickering emulsions is hampered by the current lack of understanding of the flow physics involved. This project will fill this gap and enable us to predict reactor performance for the future design of a full-scale Pickering emulsion reactor.
Deadline : 7 August 2023
(14) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Optimized Composite Material Design for Liquid Hydrogen Tank Under Mechanical and Thermo-mechanical Load
Composite Pressure Vessels (CPV) are well suited for hydrogen storage of next-generation sustainable aircraft. In this project, we will look at the filament-wound composite shell of the CPV. The project will focus on the tailoring of thermo-mechanical properties of a hydrogen storage tank for cryogenic working conditions using variable stiffness filament winding architectures. The novelty of the research will be the tailoring of thermo-elastic response of filament-wound composite structures to optimize the gravimetric efficiency of CPVs for hydrogen storage. This requires the prediction of the stress state and failure of the laminate factoring in residual stresses of the manufacturing process and thermal stresses under cryogenic (working) conditions, which need specifically developed analysis tools. Thermal stresses and different failure phenomena including failure of the vessel under cryogenic conditions will be considered in the model. Physics-based modelling approaches are being chosen in a finite element analysis framework. After validating the thermal stresses and resulting displacements for a flat plate, the complete composite pressure vessel will be modelled. The developed analysis method will be used to tailor the thermo- mechanical response of the laminate to alleviate the thermal stresses and strains.
Deadline :6 August 2023
(15) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Preliminary Design Methods of Highly Integrated Thermal Management Systems (TMS) in Aircraft for All Propulsion System Solutions
The Cluster of Excellence SE²A – Sustainable and Energy-Efficient Aviation is a DFG-funded interdisciplinary research center investigating technologies for a sustainable and eco-friendly air transport system. Scientists from aerospace, electrical, energy, and chemical engineering, as well as economics and social science are working on the reduction of drag, emissions and noise, life-cycle concepts for airframes, improvements in air traffic management and new technologies for energy storage and conversion. Technische Universität Braunschweig, the German Aerospace Center (DLR), Leibniz University Hannover (LUH), the Braunschweig University of Art (HBK), the National Metrology Institute of Germany (PTB), and Delft University of Technology have joined forces in this extraordinary scientific undertaking. The overall project is structured into the three core research areas “Assessment of the Air Transport System”, “Flight Physics and Vehicle Systems” and “Energy Storage & Conversion”.This project falls within the core research area “Energy Storage & Conversion” and aims at developing a methodology for the design of highly integrated thermal management systems (TMS) in aircraft, serving both the propulsion system, whatever concept is adopted, and the environmental control system (ECS), and exploiting advanced cooling technologies and possibly energy harvesting. The need for integrating many distributed heat sources and the design of a lightweight system call for the development of a design methodology and the related simulation and optimization infrastructure to systematically assess different TMS configurations and technologies in an early stage of the aircraft design.
Deadline : 6 August 2023
(16) PhD Degree – Fully Funded
PhD position summary/title: PhD positions Nanoscopic mapping of viscoelasticity in coatings
Society puts ever increasing demands on coatings and paints, from aerospace structures where durable coatings reduce the need for aircraft repair, to civil infrastructure where new generation wood coatings are required to promote wood as a low-carbon, fully renewable construction material. However, the development of durable coatings is challenging for two main reasons: (i) Prolonged durability assessment as a result of sub-optimal inspection methods; (ii) Lack of understanding and insight into the microstructural properties of coatings both before and after aging processes. To address these challenges, we aim at combining Atomic Force Microscopy (AFM) measurements with data-driven nonlinear dynamic methodologies to characterize viscoelastic proerpties. Our approach will go beyond ad hoc models that otherwise are used in viscoelastic characterizations and makes systematic use of machine learning to distil dissipative forces directly from AFM data over a range of frequencies, thus extracting high spatial and temporal information from the sample. By performing experiments on thin coating films under repetitive strain and different weathering conditions, we aim at tracing nanomechanical properties as a function of strain to underpin nanoscale properties that are accountable for the mechanical failure of these materials.
Deadline : 6 August 2023
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(17) PhD Degree – Fully Funded
PhD position summary/title: PhD position in theory of quantum transport
The theory of quantum transport explains the properties of nanodevices that are used as elements in modern quantum technologies, and suggests their novel non-trivial implementations. It incorporates the most channelling concepts and methods of modern theoretical physics in potentially applied context. You will make several projects in this area under supervision of Prof. Yuli Nazarov, one of the founders of the field of quantum transport. There is no pre-determined research proposal associated with these positions, this gives the freedom to formulate the projects adjusting to the interests and capabilities of the students involved.
Deadline : 5 August 2023
(18) PhD Degree – Fully Funded
PhD position summary/title: PhD position Structural Biology of Neurodegeneration
The long-term goal of our research is to understand how neurons form networks in health and disease. We address questions such as: What is the underlying structural basis of synapse formation? What happens at the nanoscale levels in diseased brain? We have recently determined cryo-EM structures of synaptic cell adhesion molecules that reveal part of the molecular recognition code underlying synapse formation. We are now also aiming to understand molecular recognition and synapse formation in a disease context using a combination of protein biochemistry/biophysics, structural biology and mammalian cell biology approaches. The successful applicant will be the driver of a new project concerning detailed structural characterization of neurodegeneration at the neuronal synapse. State-of-the-art equipment, including high-end light and electron microscopes, as well as advanced mammalian cell culture facilities are available in our department. We are an international, growing team of multidisciplinary scientists. We aim for a high impact, supportive environment, where talented people work together to solve neurobiological questions. See also our group website www.dimphnameijerlab.org.
Deadline : 1 August 2023
(19) PhD Degree – Fully Funded
PhD position summary/title: PhD student on transport phenomena in a zinc-air flow battery for electrochemical energy storage
The main objective of this project, called ReZilient, is to demonstrate the first non-critical-raw-materials-based zinc-air flow battery of its kind. It aims to fill the gap between short-term electrochemical energy storage (Li-ion) and long duration chemical energy storage (e.g. hydrogen storage). ReZilient is funded by the European Commission and is executed by a consortium of Norwegian, Danish, Finnish, Portuguese, Belgian and Dutch organisations. The PhD student will work on the electrode morphology and the modelling of the electrode and the full battery. To this end, numerical simulations will be done (80%), thereby using techniques based on the lattice-Boltzmann method. These techniques require extension to the flow battery, where transport phenomena, electrical conductivity and electro-chemistry are uniquely coupled. Moreover, an experimental facility will be developed and used (20%), and functions as a validation case for the modelling.
Deadline : Open until filled
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(20) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Cooperative and Distributed Data-Driven Control with Applications in Agriculture
To deal with uncertainties and the increasing complexity in plants to be controlled, data-driven methods have been developed to synthesize controllers from data. These data-driven control methods have a wide range of applications in both natural and man-made systems. On the other hand, control of a network of subsystems is an important topic inspired by various engineering applications. This project focuses on the development and application of cooperative and distributed data-driven control approaches in agriculture. Rapid human population growth results in high global food demand. Producing nutrient dense foods with minimal environmental impact has become crucial. As control engineers, our opportunities lie in developing advanced control approaches to increase production efficiency, reduce climate impact, and improve sustainability.
Deadline : Open until filled
(21) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Edge-AI-assisted Advanced Phenotyping
Precision agriculture, which employs sensing technology and artificial intelligence to drive automated agriculture processes and decisions, has demonstrated great potential to improve efficiency, transparency, and productivity in the sector. For example, automated crop monitoring in greenhouses can be carried out collaboratively in real time using handheld devices and autonomous robots to collect data and monitor crop health. While AI has shown promise in many context-sensing tasks, realizing the full potential of AI for automated crop monitoring requires overcoming several research challenges. These include designing computationally efficient deep learning solutions for resource-constrained edge devices and addressing the sparse data problem in training robust monitoring systems. To develop edge computing and AI-assisted solutions for resource-efficient and robust mobile crop monitoring, the Embedded System Group is offering a 4-year Ph.D. position funded by the Dutch National Growth Fund NXTGEN Hightech program. We are seeking a highly qualified candidate to work on this challenging but impactful project. The candidate will be supervised by Dr. Guohao Lan and Prof. dr. Koen Langendoen and collaborate with the TU Delft AgTech Institute and our industry collaborator Sobolt.
Deadline : July 31, 2023
(22) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Bio-plausible Local Learning Rules for Adaptive Neuromorphic Hardware
Building autonomous agents that can reliably compute and take decisions in noisy and uncontrolled environments is among the top research areas in today’s artificial intelligence (AI). Yet, doing so within constrained power budgets for battery-operated edge devices is currently an open challenge. Indeed, while current cutting-edge deep-learning approaches can now reach acceptable performance in such environments, they are still subject to adversarial attacks and need a backend of GPU clusters, thereby requiring 4 to 6 orders of magnitude more power than is allowed for by edge-computing power budgets. On both ends of the spectrum of learning algorithms are the error backpropagation (backprop) algorithm, i.e. the workhorse of modern deep learning, and local Hebbian learning rules, which are inspired by the brain’s synaptic plasticity mechanisms. The former offers excellent performance but its energy and memory footprints are incompatible with low-power edge devices, while the latter allows for low-cost hardware implementations at the expense of poor scalability beyond toy problems.
Deadline : 31 July 2023
(23) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Hardware-algorithm Co-design and Digital Neuromorphic Integrated Circuit Design
The field of neuromorphic engineering aims at replicating the brain’s key organizing principles in custom silicon toward order-of-magnitude efficiency improvements compared to current processor architectures. Based on this promise, neuromorphic engineering is now included in worldwide research roadmaps, has seen a x10 increase in yearly research output over the last decade, and fuels interest from large industrial players as well as a flourishing landscape of new startups. However, the field has not yet matured and still misses an actionable framework to deliver on its promises [1]. In this PhD project, you will directly contribute to tackling this challenge by taking inspiration from key neuroscience insight. To do so, you will look into the brain’s computational primitives at an abstraction level that is higher than individual neurons and synapses, one prominent example of which are the cortical minicolumns [2,3]. You will thus:
Deadline : 31 July 2023
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Positie Wederkerigheid Tussen Stedelijke Groei en Groene en Blauwe Infrastructuur
Dit PhD onderzoek staat in de actualiteit van de globale ecologische crisis en kijkt specifiek naar de duurzame verdichting van stedelijke gebieden (in ontwikkelingslanden) en de impact van deze verdichting op stedelijke groene en blauwe infrastructuren (uGBI). Het (ontwerpend) onderzoek kijkt naar de inpassing van uGBi in de regionale en stedelijke morfologie waarin spreiding en dichtheden, grondgebruik en -afwerking de prestatie capaciteit van uGBI bepaalt in het bijdragen aan een beter ecosysteem, watersysteem en klimaatsysteem. De schaal en positionering ven het uGBI in de stedelijke omvang, de integratie in de stedelijke typologie en dichtheid en de relatie tot de topografie zijn van belang voor het functioneren van uGBI als netwerk. Wat zijn door de schalen heen koppelingsmogelijkheden voor verschillende functies in het systeem (water, biodiversiteit, hittestress, recreatiemogelijkheden en voedselvoorziening) voor verschillende uitgangssituaties van stedelijke groei en typologie? Waar zitten de kantelpunten? Wat zijn de archetypische en nieuwe stedelijke modellen met een hoge prestatie capaciteit in het realiseren van duurzame groei en verdichting in verschillende contexten? Het ontwerpend onderzoek richt zich op het blootleggen van mechanismen van stedelijke groei en verdichting die duurzame principes van uGBI kunnen utiliseren en borgen (generiek) en de toepassing ervan op ten minste drie (kwetsbare) contexten.
Deadline : Open until filled
(25) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Integrated Seismic Reservoir Characterisation for Geothermal Reservoirs
Channelized fluvial deposits comprise some of the most productive subsurface reservoirs world-wide. Hence the geothermal project on the TU Delft campus is targeting the fluvial deposits of the Early Cretaceous Delft Sandstone to produce sustainable geothermal heat for district heating, with the excess thermal energy stored in the subsurface on a seasonal basis. Understanding how the sandbodies in fluvial deposits are connected is fundamental to robustly characterise these reservoirs and analyse how geothermal (and other) fluids move through the reservoir. However, many different geological features can reduce the connectivity of the sandbodies, for example mudstone drapes along the base of the channel, continuous mudstone beds within channel deposits, channel stacking patterns, or the sinuosity of the fluvial channels. Heat flow via conduction between fluvial channels and in zones which are not connected, plays an important role in the total energy output and the rate of decline of temperature towards the end of the project lifetime. Identifying these (small-scale) geological features from seismic data is difficult and often leads to multiple geological interpretations that cause major uncertainties during reservoir characterisation. These uncertainties need to be understood accurately because they subsequently impact key reservoir engineering decisions such as the optimal well placements in geothermal reservoirs that maximise production rates and minimise the risk of cold-water breakthrough.
Deadline :28 July 2023
(26) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Trans-stenotic Pressure Gradient Estimations
Imagine being able to help the millions of people around the world who suffer from stenotic cardiovascular disease. These patients have an abnormal narrowing of blood vessels or cardiac valves causing pressure drops that increase the heart workload, leading to cardiovascular complications and death. Your work could enable healthcare providers to differentiate between risk groups more accurately so that they know who needs urgent surgery and who does not. To do so, a correct estimation of stenotic pressure drops is essential. Unfortunately, current clinical methods are rudimental and do not account for the flow physics around the narrowing of blood vessels, nor do they take pressure recovery into account. Based in Delft, you will investigate how to improve these pressure gradient estimations using a combined advanced experimental and computational approach. As a PhD candidate in Trans-stenotic Pressure Gradient Estimations Using Experimental and Numerical Techniques at TU Delft, you will gain broad experience in experimental procedures, numerical techniques and simulations. This will give you great prospects for a professional future career in industry or academia. And the knowledge that you have truly helped to make an impact for a better society.
Deadline :24 July 2023
(27) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Aircraft Maintenance and Supply Chain Modelling
Effective management of inventory and supply chain operations in the aircraft maintenance field presents significant challenges. The intricate nature of aircraft components, stringent regulatory requirements, variable lead times for spare parts, and the critical need for parts availability to facilitate uninterrupted maintenance operations contribute to the complexity of inventory management. Moreover, the evolving technological landscape and the emergence of predictive maintenance solutions make these challenges even more intriguing to address. Successful management of aircraft inventory and supply chain demands robust systems that can accurately forecast demand, streamline procurement processes, maintain optimal stock levels, ensure proper storage and handling of components, and foster continuous collaboration among various stakeholders.This PhD position is a collaborative effort between TU Delft and KLM Engineering & Maintenance, offering you a unique opportunity to utilize your modelling skills, logistics and aviation knowledge, and teamwork capabilities to conduct impactful research. Working closely with a dynamic team in a practical setting, you will be inspired by direct collaboration with problem owners and an experienced research team devoted to aircraft maintenance planning and the development of cutting-edge decision support methodologies.
Deadline : 23 July 2023
(28) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Supersonic High-speed Centrifugal Compressors for Thermal Management Systems of Future Aircraft
The objective of this research is to conceive and develop an innovative lightweight supersonic compressor featuring a ultra-high compression ratio and extended operating range. The compressor will be developed by combining the use of advanced, CFD-based, multi-disciplinary design optimization methods with experimental activities. The candidate will be required to develop advanced numerical modelling tools and perform experiments to test prototypes of the aforementioned turbomachine in the IRIS setup. The IRIS setup implements an inverse organic Rankine cycle (iORC), also called vapor compression cycle, for cooling purposes and provides a test section for high-speed compressors and condensers.
Deadline : 23 July 2023
(29) PhD Degree – Fully Funded
PhD position summary/title: PhD Positions in the Aircraft Noise and Climate Effects Section of the Faculty of Aerospace Engineering of TU Delft
In the efforts towards sustainable aviation, we want to be able to maintain the societal benefits that aviation brings while reducing the negative environmental effects it has on its surroundings. To do so, we need to have ways to better understand and reduce the environmental footprint of present-day and future operations and technologies. PhD positions in the Aircraft Noise and Climate Effects Section of the Faculty of Aerospace Engineering of TU Delft address this challenge and focus on the noise, emissions and associated air quality and climate change aspects. The selected candidates will work towards innovative measurement and modeling approaches on these topics in close collaboration with national and international partners, which include universities, research and government institutes and the industry. The PhD position will be hosted in the Aircraft Noise and Climate Effects Section of the Department of Control and Operations of the Faculty of Aerospace Engineering. The mission of the Department of Control and Operations is to improve the safety and efficiency of operations in aerospace and to reduce the impact on the environment. The department comprises three sections: Control and Simulation, which focuses on the development of advanced automatic control systems (including the role of the human operator), Air Transport Operations, which aims to improve operational performance in terms of capacity, safety and economy, and Aircraft Noise and Climate Effects, which focuses on reducing aircraft noise, emissions and the effects of aviation on climate change and air quality.
Deadline : 23 July 2023
(30) PhD Degree – Fully Funded
PhD position summary/title: PhD Position on NLP for Supporting Climate Dialogues
Climate changes, such as sea level rise, soil subsidence, extreme rainfall, and drought, induce new challenges and risks for real estate and infrastructures, especially in low lying urbanized deltas, as in the Netherlands. While people’s homes, communities, and livelihoods are at stake, real estate climate risks also stand to destabilize markets and society at large. In this position, you will co-design and co-develop an Integrative Forum (IF) that aims to facilitate two-way transdisciplinary exchange and dialogue on climate risk management. The IF, on the one hand, provides a ‘soft space’ for debate and reflection between research teams, societal partners, and citizens at large. On the other hand, the dialogue in the IF provides an opportunity for innovative research on institutionalized logics, tactics, and procedures that hinder or enable effective cross-disciplinary collaboration.
Deadline : June 18, 2023
(31) PhD Degree – Fully Funded
PhD position summary/title: PhD position Understanding multimodal interaction with neural tissue
The successful applicant will be embarking on a new project in which multimodal electrophysiology will be used as a means to create a fundamental understanding of neural interaction. State-of-the-art equipment, including multichannel neurostimulator and recording setups, microscopy and ultrasound experimental platforms, as well as, cell culture facilities are available in our department. To be successful in this project it is foreseen that you will need to combine these into novel tailored in vitro setups, trained on performing in vitro experiments and expand your knowledge into various mechanisms of neural activation. You will be embedded in the multidisciplinary Implantable Bioelectronics research team at TU Delft, and you will collaborate with other PhD candidates and Postdocs working on several aspects of active neural implants, allowing you to expand the scope of your research project. Last but not least, you will be part of the vibrant environment of young researchers of the 10-year-long Dutch Brain Interfaces Initiative (DBI2) project, funded by the “Gravitation” programme of the Dutch Ministry of Education, Culture and Science. The project aims to bring together a broad and diverse Dutch consortium with expertise in neuroscience, neurotechnology and computer science to research brain interactions, specifically looking into understanding how each part, from neuron to brain region, interacts with the rest of the brain and with the outside world.
Deadline : Open until filled
(32) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Energy-based control for Stochastic Dynamic Networks of Neuronal Memory
This PhD project aims at developing a novel mathematical framework, inspired by energy-based control theory, for nonlinear and uncertain brain network models, in particular memory networks. Our goals are: (1) contributing to mechanistic understanding of neuronal networks of brain memory; and (2) designing novel closed-loop control algorithms for brain interface devices, in particular in applications involving memory systems such as sleep. The four-year PhD position is hosted at the RecalLab, Delft Center for Systems and Control, in close collaboration with the Donders Institute, and it is part of the DBI2 consortium funded by the Dutch Research Council. The DBI2 comprises a consortium driven by six top researchers from leading Dutch research institutes (Radboud University, Netherlands Institute for Neuroscience, Delft University of Technology, University Medical Center Utrecht, and Erasmus Medical Center), complemented by experts in the fields of neuroscience, neuroengineering, and computational sciences. The highly interdisciplinary nature of the program will enable you to actively collaborate with all partners within the consortium and learn from the best neuroscientists, neuroengineers, and computational engineers in the Netherlands.
Deadline : 16 July 2023
(33) PhD Degree – Fully Funded
PhD position summary/title: PhD position on Trustworthy and Tractable Methods for Data-driven Modeling and Control of Complex Systems
The complexity of cyber-physical systems is rising in line with the everyday advancements in technology and industry. Smart power grids, autonomous cars, distributed sensor networks, advanced robotic systems, district heating networks, and traffic platoons are only a few examples of such systems. Their primary features are high complexity, operation in complex time-varying environments, and safety-critical issues. Utilizing measurement data, one may develop model-based or model-free techniques for controlling them. One may employ purely physics-based models built from available information in the model-based strategy. To produce a model representing reality precisely, we need a comprehensive grasp of the system’s nature and a method for generating detailed models from the first principles. Due to the information limitations, this is not feasible in many applications. Also, this framework results in estimating an immense amount of parameters via non-convex large-scale optimizations formulated based on limited, heterogeneous, and noisy data, which further decreases their performance. One may utilize a reasonably simple physics-based model with few parameters to address these concerns. Though the built model may show meaningful behaviors, it may not fairly represent reality due to its excessive simplicity and inadequate inclusion of the first principles. Thus, the resulting decision-making is vulnerable to substantial bias and error. Recent machine and reinforcement learning advances have delivered promising directions for developing black-box models, particularly when complexity prevents direct physics-based description. When a massive amount of informative data is provided, advanced ML methods may identify features and structures, so that the model is interpretable. Modern ML algorithms can capture complicated spatio-temporal patterns; however, they require extensive data, which is seldom accessible in most real-world settings. Moreover, ML models may not be capable of extending to out-of-sample contexts as they only identify links in the training data. Since naïve application of ML techniques to the data results in a blind interpretation of reality, the critical underlying traits are disregarded. Thus, inconsistent results may be obtained. Hence, if there is significant uncertainty or the model structures are relatively complicated, the generated models will not correctly represent reality. Therefore, we have high inaccuracies in the models, and decision-making is vulnerable to risks. Though the inclusion of model uncertainty may address safety issues, it results in over-conservativism according to model mismatches leaked into the ambiguity envelopes. Thus, the decision-making policy will be sub-optimal. Analogous arguments exist for model-free approaches when certain aspects of reality are ignored.
Deadline : 16 July 2023
(34) PhD Degree – Fully Funded
PhD position summary/title: PhD in Social Licence to Operate Smart/vehicle-to-grid Charging
The distributed fleet of parked and plugged-in EVs, including Vehicle-to-Grid (V2G) compatible EVs, would be able to provide services to the grid in exchange for which EV drivers would be rewarded and/or financially compensated. The services provided by smart charging (demand response) and V2G would enable the integration of higher shares of variable renewable power plants and lower total cost of ownership of EVs. However, besides technical feasibility of smart charging/V2G solutions, attention should be given to social acceptance by diverse user groups to increase acceptance and scalability. Therefore, social acceptance of smart/V2G charging remains a necessary prerequisite for further adoption and commercialisation of this technology. This PhD research project is aimed at investigating social acceptance and usage of smart charging and V2G technology as well as providing recommendations on how to increase the acceptance by diverse user groups. The Theory of Planned Behaviour will provide the first direction to build an agent-based model (ABM) for social licence to operate smart/vehicle-to-grid charging.
Deadline : 15 July 2023
(35) PhD Degree – Fully Funded
PhD position summary/title: PhD position Scalable Cloud Applications
Fifteen years have passed since the very first cloud computing offering. Yet, to this date, programmers fail to har- ness the power of the cloud, struggling to grasp the ever-changing abstractions offered by cloud providers. At the moment, very few highly-skilled programmers can author scalable and consistent cloud applications. These highly-skilled programmers, however, can only be afforded by the so-called “Big Five” (Google, Amazon, Facebook, Apple, and Microsoft), leaving smaller companies and startups no chance of competing against them. Cloud programming at the moment is as hard as imperative programming was around the 1960s, before the invention of high-level programming languages. It is for this reason that one of the grand challenges in data management research is the need to reduce the entry barrier and cost of building scalable cloud applications. At the time of writing we are experiencing a global shortage of software developers, while businesses and organizations move their workloads to the cloud. Democratizing the use of the cloud is timely and of critical importance.
Deadline : July 15, 2023,
(36) PhD Degree – Fully Funded
PhD position summary/title: PhD Positie Hersendynamica van het Migraine Brein
Om beter te leren begrijpen hoe de hersenen werken moeten we weten hoe elk deel van de hersenen samenwerkt met andere delen, en hoe informatie van binnen of buiten het lichaam verwerkt wordt. Wanneer er binnen deze processen iets fout gaat kan dit leiden tot neurologische aandoeningen, zoals migraine. Migraine is een veelvoorkomende aanvalsgewijze hersenziekte. Een migraineaanval kan zich op ieder moment voordoen en heeft daarmee vaak een enorme impact. De vatbaarheid voor een aanval verschilt per persoon en per aanval en wordt bepaalde door de zogenaamde ‘migrainedrempel’. Hoe hoger iemands ´migrainedrempel’, hoe kleiner de kans op een aanval. Er bestaan echter nog geen betrouwbare manier om deze migrainedrempel te bepalen. Kennis van de migrainedrempel kan in de toekomst helpen bij het voorspellen van een aankomende migraine aanval en daarme behandeling verbeteren. Binnen het Migraine@Home project gaan we op zoek naar methoden om de migrainedrempel nauwkeurig te kunnen meten door gebruik te maken van metingen van hersenactiviteit (EEG) in combinatie met geavanceerde system identificatie technieken. Als een PhD student binnen het Migraine@Home project zal je je aandacht richten op het ontwikkelen en toepassen nieuwe (EEG) meetmethoden en system identificatie technieken om het migraine brein beter te leren begrijpen. Jouw ontwikkelde meetmethode kun je direct toepassen in de klinische omgeving door het uitvoeren van metingen in het lab of de thuisomgeving van de patient.
Deadline : Open until filled
(37) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Hybrid Intelligence and Affective Computing with a focus on Social-Emotional Memory Modelling for Conversational AI
We are seeking a highly motivated and talented PhD researcher to work on an exciting project focused on hybrid memory modelling based on human affective responses to within deliberative conversations. The successful candidate will be part of a dynamic team of researchers at the forefront of research in hybrid intelligence and affective computing. The project aims to investigate the creation of long-term common ground between humans and a conversational AI in multi-part conversations. The successful communication in such settings depends on understanding what is relevant from the human and what is relevant from an AI perspective, and aligning this information in a Theory of Mind (ToM) model. The candidate will work on developing and implementing computational models that can leverage human affective responses to identify and interpret potential conflicts that might arise in the conversation. The succesful candidate will be part of the Hybrid Intelligence Center and work in close collaboration also with researchers at the Vrije Universiteit Amsterdam.
Deadline : July 14, 2023.
(38) PhD Degree – Fully Funded
PhD position summary/title: 2 PhD positions Multi-Sensor Multi-modal Machine Learning for Social & Affective Computing
Enabling machines to interpret social behaviour in real life is a major frontier of Modern Artificial Intelligence (AI). Unfortunately, there is almost no attention on developing systems to infer intentions. Those claiming to do so are labelling them by observing the future. This is problematic because they cannot detect unrealised intentions or unintended outcomes. Intention estimation is extremely challenging because inferences need to be explained with respect to situational context. This project develops a novel framework and learning approaches to learn narratives of intention from multiple perspectives in in-the-wild ecologically valid social situations such as social networking events.
Deadline : July 12 2023
(39) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Extreme Mechanics of Ultralight Architected Materials
Ultralight architected materials leverage carefully engineered artificial structures (inspired from bone, bamboo, etc.) miniaturized to micro/nano material lengthscales to unlock unprecedented and tailored properties such as high strength and toughness at very low relative densities. However, little attention has been devoted to understanding the behavior of these materials under extreme deformations — particularly since existing theories for continuum materials breakdown due to the presence of architected microstructures. Understanding such behavior is critical to enable the adoption of such ultralight architected materials in the aerospace industry. Previous efforts in our research have furnished unique capabilities to design architected materials with tailored properties and to model complex material behavior in extreme environments with unprecedented fidelity. Building on these results, the goal of this project is to develop a scalable simulation capability as well as analyze the mechanical behavior of architected materials under extreme deformations. The outcome of this project will pave the road towards the utilization of this technology in novel ultralight aerospace structures.
Deadline :10 July 2023
(40) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Enhancing Machine Learning Education in Computer Science
Faced by abundant use of machine learning in industry and academia, the effective and efficient teaching of core concepts in this field becomes important. The Computer Science & Engineering curricula include many ML courses, thereby preparing the software engineers to use ML in their work. The growth of the number of courses is also visible in the undergraduate CSE programs, aiming on one hand to prepare students for applying ML in the industrial context and on the other hand to prepare students for the master programmes. However, there is little structured information and published work on (1) what the needs of the CS and AI master programmes and industry are, in terms of expected Machine Learning knowledge, (2) what ML undergraduate education should achieveand how, and (3) how to structure curricula containing ML courses and effectively teach Machine Learning in the undergraduate CSE programs. This project will focus on gaining insight into those 3 categories of unknown. You can choose to explore the topics such as existing strategies/instructional designs to teach ML, identifying basic concept and skills, and their order in the Computer Science curricula, reliably recognizing progress in ML learning, discovering the pre- and misconceptions that influence learning of ML, etc. Based on the results of empirical studies you will propose innovations in teaching Machine Learning and evaluate them in Machine Learning courses.
Deadline : July 9, 2023
(41) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Multi-hazard Resilient and Low Carbon Façade Technologies
You will work on developing multi-criteria decision making workflows to support the selection and design of resilient and low-carbon façade technologies in new and existing buildings. This involves considering various natural hazards such as earthquakes, as well as climate-related threats like heat waves and floods. By leveraging these workflows, the candidate will contribute to the creation and demonstration of innovative low-carbon responsive façade technologies that significantly enhance the preparedness and responsiveness of buildings across diverse climatic conditions. In addition, you will assist other researchers in the monitoring of façade and building performance pre and post intervention. You will hold an joint appointment within two research groups at the Faculty of Architecture & Built Environment, namely: the Sustainable Architectural Materials & Structures group (ReStruct Group) and Architectural Facades and Products group (Architectural Facades & Products). The position is funded for a duration of 4 years, during which the appointed candidate will (i) undertake research on their PhD topics and (ii) assist with the educational and research agenda of MULTICARE and the host groups. The project will offer opportunities to collaborate with industrial partners and academics from other disciplines, as required (human comfort and building physics, façade design and engineering, climate design, probability and uncertainty analysis, seismic and structural design).
Deadline : 9 July 2023
(42) PhD Degree – Fully Funded
PhD position summary/title: PhD position in Organic Bioelectronics
The aim of our research is to develop multifunctional and multidimensional bioelectronic interfaces that can be used to answering fundamental questions in biology, and to developing technologies that can treat diseases. By combining organic electronic materials with silicon-based electronic technologies, we aim to develop electrically and optically active devices that can interact with human cells and tissue, with high spatial and temporal resolution. You will be embarking on a new project in which bioelectronic chips combining organic electrochemical transistors and light-sensitive devices are targeted. The first two years you will design and fabricate these chips in our fully equipped cleanroom as well as characterize these devices in our electrical and optical characterization labs. Later on, you will use these chips to monitor and/or stimulate human cell cultures in vitro, using electrical measurements and wireless light stimulation. More specifically, you will examine how electrical and optical cues can interfere with neuronal electrophysiology as well as how these cues can be used to regenerate damaged neurons from human adult stem cells.
Deadline :July 9, 2023
(43) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Spatial Decision Support System for Multi-Hazard Resilient Cities
You will develop a novel robust spatial decision-making framework, involving risk analysis and cost-benefit estimations, for quantifying the multi-hazard resilience of buildings and for supporting intervention planning at large scale. A Spatial Decision Support System (SDSS) will be developed for multi-hazard solutions and strategies, with a particular focus on climate change-related hazards (floods, heatwaves, storms). A Spatial Data Infrastructure of the SDSS will be built upon an existing Digital Twin platform. This Spatial Data Infrastructure focuses mainly on the efficient management of big static (such as hazard related maps, 3D building models, etc.) and dynamic (real-time sensor) spatial data on urban scale through spatial DataBase Management Systems. Furthermore, it explores the potential of OGC standards to harmonize, share and integrate the heterogeneous (static and dynamic) spatial data which will be incorporated into the Spatial Decision Support System for resilience-based multi-criteria decision analyses. You will hold a joint appointment at two groups at the Faculty of Architecture & Built Environment, namely: the Sustainable Architectural Materials & Structures Group (ReStruct Group) and the GIS Technology Group (Architectural Facades & Products). (S)he will work in a team closely collaborating with industrial and academic partners associated with MULTICARE. The position is funded for a duration of 4 years, during which the appointed candidate will (i) undertake research on their PhD topics and (ii) assist with the educational and research agenda of MULTICARE and the host groups at Department of Architectural Engineering + Technology
Deadline : 9 July 2023
(44) PhD Degree – Fully Funded
PhD position summary/title: PhD Position Dynamic Modelling and Design of Smart Metamaterial for Impact-absorbing Mounts
Resilient mounts (shock mounts) are used to connect machinery to a ship’s decks, and to prevent noise and vibration generated by the machinery operation from travelling through the ship structure. For naval vessels in particular, these mounts should also protect equipment against the large displacements imparted by underwater explosions. These diverse objectives often generate conflicting requirements regarding the mechanical behaviour of these mounts, which should vary for different frequency ranges. The range of needs becomes even wider when considering other types of machinery, for instance radar systems. Although the technology has been used for many years, there has been little development in adapting the designs for modern applications. These mounts are designed based on semi-empirical methods, meaning that the current designs cannot be extrapolated to satisfy significantly different challenges and requirements. This project will develop theoretical modelling techniques based on existing experimental data, to enhance our understanding of the complex dynamic behaviour of these mounts. It will then utilise this knowledge to develop optimised metamaterials that have unique dynamical properties and can be used for tailored solutions. We are looking for an enthusiastic PhD candidate to be the main developer of the analytical & numerical methods that will allow us to better understand these challenges. The outcomes of this project will be disseminated to the scientific community and to a general audience through presentations at (inter-)national conferences and through publications in peer-reviewed journals. Additionally, the candidate is expected to take part in educational activities within the department (for example, assist in teaching or supervise master thesis work).
Deadline : 7 July 2023
(45) PhD Degree – Fully Funded
PhD position summary/title: PhD Position in Robustness and Control of Probabilistic Machine Learning Models
TU Delft is a top tier university and is exceedingly active in the field of Artificial intelligence and Control Systems. The HERALD lab is devoted to the development of novel computational frameworks to enable AI-based systems to safely and robustly interact with the humans and the uncertain environment around them. Our long-term ambition is to lay a foundation for the development of future autonomous systems that can reliably and beneficially interact with humans. On this PhD project you will investigate the combination of probabilistic methods and formal methods from computer science and control theory to devise solutions to problems in the context of data-driven control systems. In particular, the project will shift towards Bayesian (deep) models for enabling probabilistic reasoning over the correctness of AI based control systems.
Deadline : 8 July 2023
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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