KTH Royal Institute of Technology, Stockholm, Sweden 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 KTH Royal Institute of Technology, Stockholm, Sweden.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Networked Systems Security
We invite applications for doctoral student positions with the Networked Systems Security (NSS) lab at EECS/SCS. We are looking for highly motivated individuals to pursue a PhD in security and privacy. The positions involve activities leading to original research and results in peer-reviewed publications. The research topics can relate with any of the NSS group areas (https://www.eecs.kth.se/nss).
The NSS group designs and builds trustworthy networked systems, with a research agenda covering a gamut of security and privacy problems and results that got significant attention by the research community. NSS introduced a new security curriculum at KTH. Candidates with experimental/systems or theoretical profiles and research interests in any aspect of security and privacy are welcome to apply.
Deadline : 17.Nov.2024
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in sprayed solar cells
In our group, we focus on organic and electronics and solar cells based on sustainable, bio-based materials. Starting from the thinnest conductive paper, we use advanced thin film technologies to apply functional layers by spray deposition as rapid, versatile, scalable, and industrially relevant thin film technology. This project aims to develop novel all-sprayed solar cells. Here, it is crucial to elucidate the underlaying nanostructure-process-functionality (e.g. photovoltaic efficiency) relationship; thus, the work includes design and development of spray deposition for cellulose-based polymer solar cells including in situ experiments at synchrotron radiation facilities, such as MAX IV in Lund, at synchrotrons in Germany, Europe, and USA as well as neutron scattering at European and international neutron sources. We thus expect the student to travel abroad for experimental work.
Deadline : 14.Nov.2024
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in secure machine learning for cyber-physical systems
Foundation models, many of them based on self attention, have revolutionized natural language processing (NLP) and excel in other application domains, such as computer vision and time series forecasting. Their integration in future cyber-physical systems (CPS) has great potential, but it also brings unprecedented risks due to potential vulnerabilities. In this project project we will explore the vulnerabilities of foundation model (e.g., transformers) enabled CPSs and will develop detection and mitigation schemes to enable robust CPS including ML components. The PhD student is planned to spend at least 13 months at Nanyang Technological University (NTU) during the project.
The Division of Network and Systems Engineering conducts fundamental research in networked systems, wireless communications and cyber security. Industrial projects involve partners such as Ericsson, Atlas Copco and Telenor. Part of the research is conducted within the framework of the Wallenberg AI, Autonomous Systems and Software Program and in Digital Futures. We have an extensive academic network and collaborate with researchers at MIT, UIUC, Stanford, EPFL, among others.
Deadline : 11.Nov.2024
(04) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in matrix computations for data science
Computation is often regarded as a third approach in science, complementing theory and experiments. In recent years, new data analysis tools have emerged as a fourth approach, enabling solutions to problems that were previously beyond reach using traditional methods. Many scientific challenges in computation and data analysis are expressed through linear algebra, with solutions derived from algorithms in numerical linear algebra. In this project, you will focus on developing numerical algorithms for data analysis. These algorithms will incorporate a combination of numerical linear algebra techniques, expressed as matrix problems and matrix functions, alongside machine learning methods, including deep learning neural networks, and approaches specific to particular domains. As a doctoral student, you will join the computational mathematics research group at KTH’s Department of Mathematics, where you will have opportunities to collaborate with other doctoral students and experts in numerical linear algebra. This project is funded through the SeRC (Swedish e-Science Research Center) collaboration platform, which provides access to, for example, expertise in deep learning and data from coastal oceanography in partnership with Stockholm University. This will give you the chance to engage with data related to important societal issues, such as climate and environmental challenges. Research involves developing software, algorithms, theory, and conducting analysis to tackle significant problems. The balance between theory, methods, or applications will be shaped by your interests as a doctoral student.
Deadline : 07.Nov.2024
(05) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in software retrofitting for converters
The electricity grid is undergoing rapid changes due to the implementation of renewables and large increase of power electronic converters. For example, High-Voltage Direct-Current (HVDC) technology is a key enabler for offshore wind integration and has massive expected growth rates. HVDC converter systems that are put in operation are normally expected to last 20-30 years, however during this time the surrounding electricity grid will change. For that reason, it is likely that the converter control and protection software needs to be adapted. In this project we will investigate the boundary conditions, such as initial hardware overdesign, energy storage, or tradeoffs between required and added functionalities. Ideally, the converter control and protection software is designed such that as little as possible or no adaptation will be needed. A deeper understanding between power electronics design and high-level functionality in the power system is a main outcome of the project. The studies are supposed to be executed in computer simulation and verified in a down-scaled laboratory prototype.
Deadline : 04.Nov.2024
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(06) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in HVDC grid protection expansion
High-voltage direct-current (HVDC) technology is a key enabler for the integration of offshore wind energy into our power system. Today, we see the first multi-terminal systems appearing, however their design is typically done for the complete system and “plug&play” is not yet available. In this project we want to investigate the design of HVDC protection in multi-terminal systems, in particular during expansion. Here, both software and hardware parameters need to be considered and topics such as software re-design vs hardware re-design (or both), single- vs double-ended fault detection, and interaction with converter controls need to be taken into account. The outcome of the project is a deeper understanding of plug&play HVDC protection design. The studies are supposed to be executed in computer simulation and verified using a protection intelligent electronic device.
Deadline : 04.Nov.2024
(07) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Electrical Engineering
WELD-AID is a Vinnova-funded project that aims to revolutionize quality assurance in welded structures by integrating Artificial Intelligence (AI) and digitalization. Through collaboration with industry partners, it seeks to reinforce Sweden as a global leader in designing and manufacturing welded products, ensuring superior mechanical performance and production quality. The project will explore using advanced machine learning (ML) methods, including deep neural networks (DNNs), to detect weld imperfections and optimize the welding process. Within the grander research project, the advertised doctoral student position will develop hybrid models involving convolutional neural networks (CNNs) for weld localization and use physics-informed neural networks (PINN) for weld quality assessment, including service life predictions for welded structures.
Deadline : 01.Nov.2024
(08) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in nuclear materials science
Doctoral student in nuclear materials science
We are looking for a PhD student to work on integrating the use of Small Modular Reactors (SMRs) to industrial processes, such as pyrolysis and hydrothermal carbonization (HTC), to produce high quality renewable products including bio-coal and bio-oil. The PhD project is aimed at evaluating structural materials that will interact with different potential heat extraction media in terms of stability, thermal performance, and efficiency, followed by pyrolysis and HTC experiments in collaboration with the project partners.
The PhD student will collaborate nationally as well as internationally with several different partner universities and companies, including the project partners: the Research Institutes of Sweden (RISE), Blykalla, and RISE Processum AB. This project will also be connected to the Nuclear Materials Platform NuMaP. The student will be part of a multidisciplinary team of researchers and students.
The project contains both modeling and experiments, directly or indirectly. Multiscale modeling (atomic scale and finite element modelling) will form the basis of the theoretical part and will be compared with experimentally obtained results. Some materials are developed within the projects.
Deadline : 01.Nov.2024
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(09) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in radar systems
The project focuses on beam-forming techniques for modern antenna-array based radar systems for various civil and defence applications. Different techniques, including machine-learning, will be investigated for advanced digital, analog and hybrid beam-forming concepts, and applied to generating arbitrary beam-shapes in multi-static and passive radar systems. The work is primarily to be done by using simulation and computation software. If a hardware platform will be available before the end of the project, an implementation of the techniques on that platform will be carried out. The project requires a background in electrical engineering, radar systems, electromagnetic simulations, basic signal processing techniques, and preferably also machine learning.
The work is carried out in the THz lab of the Micro and Nano Systems division at the Dept of Intelligent Systems at KTH School of Electrical Engineering and Computer Science. The lab has measurement equipment, incl. a fully automated THz antenna measurement chamber, up to 750 GHz, and many custom-built microwave and THz measurement setups. The project work is carried out in tight collaboration with Swedish radar industries within civil (car radar, security radar, THz imaging) and also defence applications.
Deadline : 31.Oct.2024
(10) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student (licentiate) in Transport Science
The adoption and integration of innovative supply chain and logistics solutions are pivotal for the manufacturing industry in its ongoing transition towards sustainable practices.
The Division of Transport and Systems Analysis at KTH is seeking a highly qualified candidate for a doctoral position for a project in Transport Logistics. The applicant will investigate innovative logistics solutions to address critical logistics challenges for performance and sustainability. The primary methods will involve simulation (e.g., agent-based models) and optimization, which also includes techniques from machine learning. The doctoral project will be conducted in collaboration between academic institutions and industry.
Deadline : 31.Oct.2024
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(11) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Hybrid Energy Storage
To combat climate change, the power grid is evolving and many countries around the world are increasing their utilization of energy storage systems. Energy storage systems can, at various levels of the power grid, provide different services directly to the user but also to the grid itself. However, not all types of storage systems are equally suitable in all situations, e.g., an inherently large power capability is not naturally coupled with a high energy capacity and vice versa. The approach of combining different types of storage systems is called hybrid energy storage system (HESS). This allows for more flexibility and opportunities but can, e.g., increase losses if not properly handled. The development of new electrochemical batteries, hydrogen storage and fuel cells, electrical vehicles (and their connection to the grid via Vehicle-to-Grid, V2G), new services (for stability and sustainability) and actors in the grid all allows for interesting possibilities where HESS can contribute.
In this PhD project hybrid energy storage systems will be investigated for different applications in the grid, their ability to provide different services with optimized performance over time, reduced climate impact and/or maximized revenue possibilities.
Deadline : 31.Oct.2024
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(12) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student (licentiate) in Transport Science
Urban mobility is changing rapidly due to new business models, and upcoming technological advancements involving energy, connectivity, and autonomy. “Smart mobility solutions”, which rely on on-demand services and new technologies, will become increasingly important for creating more efficient and sustainable transportation systems. Examples include shared mobility and crowdsourced delivery services.
The Department of Urban Planning and Environment at KTH is seeking a highly qualified candidate in the field of Transportation. The applicant will research innovative solutions for urban transportation to address crucial challenges such as economic viability, accessibility, equity, and pressing environmental goals. The project will focus on policy design and evaluation for passenger transportation through agent-based simulation or simulation-based optimization for last-mile logistics, as well as the design of efficient mobility services and their implications for sustainable urban systems.
Deadline :31.Oct.2024
(13) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Electrical Engineering
The significant progresses in 5G, cloud, and AI are revolutionizing the world of industrial automation and robotics. However, there are still major concerns about safety, security, and availability before the 5G, cloud, and AI technologies can be used in safety-critical applications such as industrial control systems and massive infrastructures. This challenge has become even more crucial for sustainability, humans, and the environment because we need to cope with the impacts of not only unintentional interferences in the infrastructure but also malicious attacks. The safety concern is the root cause of the stringent requirements on latency, reliability, security, and resilience in general.
In this project, we hope to crack this “hard nut” by interdisciplinary innovations. Our goal is to guarantee both functional safety and uptime of the safety-critical automation systems controlled over wireless, cloud, and AI. Therefore, we will not limit ourselves to a certain discipline or tool, instead, we will leverage the co-design of communication, computing, and control with the generative artificial intelligence tools. We will explore from two directions, 1, how to make the infrastructure aware of and optimized for safety applications, 2, how to make the safety applications aware of and optimized for the insufficiencies of the infrastructure.
The use cases include autonomous robots and machines, automated mobile vehicles for manufacturing, hoisting and ventilation systems for mining, remote operation of cranes at container terminals, heavy-duty grinding motors and drives, and large petrochemical systems, to name a few. We aim not only for theoretical breakthroughs but also leaps in industrial practice through real-life proof-of-concepts with our industrial partners.
Deadline : 31.Oct.2024
(14) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in light-controllable fluorescent proteins in microscopy
We are looking for a motivated student interested in biophysics and live-cell imaging to join the Advanced Optical Bio-Imaging Laboratory (http://www.testalab.org) in Stockholm as a PhD student. The student will be part of the Biophysics Unit of the KTH Royal Institute of Technology, located in SciLifeLab in Stockholm, a national hub for life-science excellence in Sweden with cutting-edge laboratories and a vibrant multidisciplinary environment.
The student will investigate the properties of fluorescent probes and how we can use them to develop advanced imaging strategies for the study of cellular processes. The project will focus on the development of different spectroscopic and imaging assays to study the kinetics of photo-controllable fluorescent molecules. Experimental work on optical devices, in the wet lab and on image analysis will be integral parts of the project.
Deadline : 30.Oct.2024
(15) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Machine Learning for Cancer Genomics
We are looking for a highly motivated and ambitious PhD candidate to join our team and contribute to cutting-edge research in machine learning and deep learning. This is a fully funded PhD position within the Division of Computational Science and Technology at KTH. Successful candidates will also become part of the SciLifeLab community, Sweden’s leading research infrastructure for life sciences.
As a PhD student, you will focus on exploring and developing advanced machine learning frameworks to improve our understanding and identification of key genes, modules, and pathways in cancer, utilizing transfer learning techniques. Your research will contribute to the following areas:
- Graph Convolutional Networks
- Graph Embedding
- Transfer Learning
If you are passionate about advancing AI in biology and eager to work in a collaborative and interdisciplinary environment, we encourage you to apply and join us in addressing these exciting challenges.
Deadline : 25.Oct.2024
(16) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Machine Learning for Drug Interaction Prediction
We are looking for a highly motivated and ambitious PhD candidate to join our team and contribute to cutting-edge research in machine learning and deep learning. This is a fully funded PhD position within the Division of Computational Science and Technology at KTH. Successful candidates will also become part of the SciLifeLab community, Sweden’s leading research infrastructure for life sciences.
As a PhD student, you will work on developing innovative machine-learning and deep-learning frameworks to enhance our understanding and prediction of drug interactions and adverse reactions using diverse data types, including Sweden’s comprehensive drug interaction dataset. Your work will focus on the following areas:
- Graph Convolutional Networks
- Graph Embedding
- Multimodal Learning
If you are passionate about advancing AI in pharmacology and eager to work in a collaborative and interdisciplinary environment, we encourage you to apply and join us in addressing these exciting challenges.
Deadline : 25.Oct.2024
(17) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Machine Learning for Biological Image Analysis
We are seeking a motivated and ambitious PhD candidate to join our research team and work on cutting-edge Machine and Deep Learning topics. The positions are based at the Division of Computational Science and Technology at KTH, and the successful candidates will also be part of the SciLifeLab community, Sweden’s leading research infrastructure in life sciences.
As a PhD student, you will be working on image-based profiling and generative modeling of biological imaging. You will contribute to the development of machine learning frameworks to address key challenges such as biological phenotype classification, feature embeddings, interpretability of visual differences between biological images. This position offers an exciting opportunity to contribute to the research areas of guided data generation for targeted applications and/or self-supervised learning for large-scale data.
If you are passionate about pushing the boundaries of AI in biology and eager to work in a collaborative, interdisciplinary environment, we invite you to apply and join us in tackling these exciting challenges.
Deadline : 24.Oct.2024
(18) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Solid Mechanics
We are looking for a doctoral student to develop and validate numerical tools for examining the mechanical behaviour of fibre network materials across all relevant length scales, from the fibre joint level to the continuum, in the “Concurrent Multiscale Micromechanical Modelling of Fibre Networks” project. The project aims to advance a concurrent multiscale approach, incorporating plasticity, hygroexpansion, viscoelasticity, and damage at both the fibre and fibre connectivity scale.
In this project, you will investigate the effects of drying and moistening on fibres and fibre joints during manufacturing and end-use. A particular focus will be on understanding how anisotropic constraints during drying influence the development of the network’s mechanical properties and residual stresses. This project provides an exciting opportunity to gain insights into the complex behaviour of fibre networks and contribute to advancing our understanding of these widespread materials, which are integral to the transition towards sustainable development.
You will develop and extend our in-house computational tools for multiscale modelling of fibre networks. On the experimental side, you will have access to state-of-the-art facilities for material characterization, including advanced imaging techniques and mechanical testing equipment necessary for completing and validating the modelling efforts.
This project will establish close collaboration with industry partners such as Tetra Pak, Canon, RISE and FibriTech. The doctoral student is expected to disseminate their work through publications in scientific journals and contributions to national and international conferences.
Deadline : 24.Oct.2024
(19) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in semantic communications and artificial intelligence
We are seeking 1 PhD student with a strong background and interest in Mathematics, Optimization, Machine Learning, and Wireless Networks. The following research directions will be investigated:
- Semantic Communications and Artificial Intelligence for future wireless networks
The PhD projects will be sponsored by the Vinnova SweWinn 6G research center of KTH, which involves leading KTH and Ericson scientists.
Deadline : 24.Oct.2024
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(20) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in traffic optimization using machine learning
The Division of Decision and Control Systems is currently looking for a doctoral student with a strong background and interest in mathematics, signal processing, and traffic models. The successful candidate will join a world-renowned team on an interdisciplinary project focused on tackling complex traffic problems using control methodologies including machine learning. We are pioneers in traffic estimation using physics-informed machine learning and we ambition to leverage this technique for enhancing traffic flow forecasting, optimizing vehicle routing and scheduling, or improving last-mile delivery in real-world traffic scenarios.
Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s
largest individual research program ever, a major national initiative for strategically
motivated basic research, education, and faculty recruitment. The program addresses
research on artificial intelligence and autonomous systems acting in collaboration
with humans, adapting to their environment through sensors, information and
knowledge, and forming intelligent systems-of-systems.
The vision of WASP is excellent research and competence in artificial intelligence,
autonomous systems and software for the benefit of Swedish society and industry.
Deadline : 24.Oct.2024
(21) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Transport and Energy Optimization
The transport sector is undergoing a rapid shift towards electrification to reduce CO2 emissions, especially within freight transport. This PhD position focuses on developing optimization models and algorithms to improve route planning and energy efficiency in electrified transport networks.
Deadline : 24.Oct.2024
(22) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in nanostructure electrosynthesis for organic oxidation reactions
Water-splitting electrolysis is a promising technology to produce green hydrogen fuel. However, this technology is limited by the slow kinetics of the reaction at the anode, i.e., the oxygen evolution reaction (OER). Alternatively, the oxidation of renewable biomass-derived organics, such as glycerol, requires lower applied overpotentials and produces industrially valuable chemicals. This project aims to prepare bimetallic and trimetallic catalysts to perform organic oxidation reactions coupled with hydrogen production at the cathode. Non-toxic and affordable solvents, such as deep eutectic solvents, will be explored to prepare bi- and trimetallic nanostructures with tuned morphology and composition using the electrodeposition technique. In this project, we will evaluate how the composition and catalyst structure affect product selectivity and stability under different applied potential conditions. For further details of the employed methodology, read more here.
This recruitment is connected to the Wallenberg Initiative Materials Science for Sustainability (WISE). WISE, funded by the Knut and Alice Wallenberg Foundation, is the largest-ever investment in materials science in Sweden and will encompass major efforts at Sweden’s foremost universities over the course of 10 years. The vision is a sustainable future through materials science. All early-stage researchers recruited into the WISE program will be a part of the WISE Graduate School, an ambitious nationwide program of seminars, courses, research visits, and other activities to promote a strong multi-disciplinary and international network between PhD students, postdocs, researchers, and industry.
Deadline : 24.Oct.2024
(23) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Affinity Proteomics
The projects will be focused on various aspects of highly multiplex protein and antibody profiling in association to studies of infectious diseases and vaccinations. The PhD-student will be based at the Division of Affinity Proteomics, Department of Protein Science, KTH campus Solna at SciLifeLab, Division of Affinity Proteomics | KTH. Projects will be done within international collaborations, such as with Professor Laurent Renia at Nanyang Technical University in Singapore, where parts of the PhD also will be performed. Projects will be based on generation and advanced data analysis of extensive protein and antibody profiles in different disease contexts. In-house developed suspension bead array technologies will be utilized with either viral or human antigens for multi-disease serology and autoantibody profiling, or antibodies targeting the human proteome coupled to beads. All human antigens and antibodies are generated within the Human Protein Atlas. There will be a focus on mosquito spread diseases, currently mainly affecting tropical and subtropical areas, such as dengue, zika and chikungunya, but also malaria.
Deadline : 24.Oct.2024
(24) PhD Degree – Fully Funded
PhD position summary/title: PhD Student in Machine Learning for Computational Biology
We are seeking a motivated and ambitious PhD candidate to join our research team and work on cutting-edge Machine and Deep Learning topics. The positions are based at the Division of Computational Science and Technology at KTH, and the successful candidates will also be part of the SciLifeLab community, Sweden’s leading research infrastructure in life sciences.
As a PhD student, you will be working on the investigation and development of innovative machine learning frameworks aiming at enhancing our understanding of cellular behavior and interactions. The projects will involve integrating diverse data types, including microscopy images and omics data, to simulate and predict biological processes. You will contribute to the research areas of multimodal learning and cross-modal integration and/or graph-based generative models.
If you are passionate about pushing the boundaries of AI in biology and eager to work in a collaborative, interdisciplinary environment, we invite you to apply and join us in tackling these exciting challenges.
Deadline : 24.Oct.2024
(25) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Fluid and Climate Theory
This Ph.D. position is integral to the HumanIC European training network. The initiative aims to cultivate early-stage engineers and scientists, adopting a novel, human-centric approach to indoor climate in Healthcare Environments. This position focuses on sustainable methods for designing and managing indoor environments characterized by a favorable thermal climate and minimized airborne particle spread. The approach combines laboratory measurements with numerical simulations, specifically Computational Fluid Dynamics (CFD) and advanced Visualization techniques, including Virtual and Extended Reality (VR and XR), to render the invisible aspects of indoor air quality perceptible and heighten awareness of its health impacts on occupants. The emphasis is on designing and optimizing building ventilation to enhance indoor air quality and thermal comfort, mitigate respiratory disease transmission, and ensure a healthy indoor climate for occupants.
Deadline : 18.Oct.2024
( 26) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Engineering Materials Science
We are looking for a PhD student in material characterization and its connection to physical modeling at the Hultgren Lab unit of the Department of Materials Science and Engineering. This doctoral position is aimed at enabling sustainable industrial transformations by establishing high-throughput material characterization research at large-scale synchrotron and neutron facilities, e.g. PETRA III in Hamburg and ILL in Grenoble, and also in lab-scale. This will involve the development of new methods and test environments to be able to simulate industrial manufacturing processes and to be able to study the microstructure development of materials effectively. Furthermore, new analysis approaches will be developed to be able to handle the large amount of data generated in the large-scale experiments, and so we will explore AI methods for analysis and work with automation as far as possible. Therefore, interest in both experimental measurements as well as analysis and programming is important for this doctoral position. The high-throughput methodology that will be developed is material agnostic, but we will apply the methodology to advanced steels, nickel superalloys and cemented carbide for proof-of-concept and to generate important information for sustainable transformations in these material areas.
Deadline : 17.Oct.2024
(27) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in Optimization and Control of Complex Systems
The Division of Decision and Control Systems is seeking two PhD candidates to work at the intersection of optimization, game theory, and automatic control, for the analysis, modeling, and control of complex systems, such as energy and transportation. The project will involve developing both mathematical foundations, namely, innovative algorithmic and control methodologies, and engineering solutions, namely, their adaptation to practical application domains.
Deadline :17.Oct.2024
(28) PhD Degree – Fully Funded
PhD position summary/title: Doctoral students in learning and optimization for edge computing
The Division of Network and Systems Engineering is looking for two doctoral students with a very strong background and interest in system modeling, optimization, and machine learning. The successful candidate will join a MSCA Doctoral Network project on developing rigorous, novel tools for safe and prompt learning and optimization of distributed network infrastructures. A strong focus will lie on the development of algorithms that include machine learning components, and on cooperation with industrial partners and with the TECoSA competence center at KTH.
The Division of Network and Systems Engineering conducts fundamental research in networked systems, wireless communications and cyber security. Industrial projects involve partners such as Ericsson, Atlas Copco and Telenor. Part of the research is conducted within the framework of the Wallenberg AI, Autonomous Systems and Software Program and in Digital Futures. We have an extensive academic network and collaborate with researchers at MIT, UIUC, Stanford, EPFL, among others.
Deadline :13.Oct.2024
(29) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Molecular simulation of wetting
The energetics and dynamics of wetting are of high interest from both fundamental scientific and technological points of view. Often the macroscopic behavior of wetting is dominated by processes at molecular scale at the interface. Here in particular the three-phase contact line is of importance, where substrate, liquid and gas (or another liquid) meet. The scales involved can not be observed directly in experiments. Atomistic molecular dynamics simulations can provide the required spatial and time resolutions. In this project we will study how mixtures of two liquids or a liquid plus surfactant wet surfaces. The surfaces can be either simple or have molecules attached to them that interact with the liquid in different ways. In experiments non-trivial behavior has been observed. There can be several reasons for this, e.g. preferential wetting of the substrate by one of the liquids (energetics), but also differences in dynamic behavior of liquids at the contact line. Molecular dynamics simulations enable us to disentangle such effects and will allow to rationalize behavior observed in experiments. As a further step, we might be able to capture certain types of behavior as boundary conditions or extensions of continuum models, which are being developed together with collaborators at the department of Mechanics.
Deadline :07.Oct.2024
(30) PhD Degree – Fully Funded
PhD position summary/title: Doctoral student in Nuclear Power Safety
The group of Nuclear Power Safety in Nuclear Science and Engineering (NSE) Division conducts research on accident phenomena of risk importance to existing and future nuclear reactors, and performs safety analysis for nuclear power plants, including design basis accident analysis and severe accident analysis. The research projects involve extensive international cooperation.
We are looking for two doctoral students to carry out frontier research on (ii) interactions of core melt with below-RPV structures during severe accidents of light water reactors; and (ii) natural circulations in advanced water and lead cooled nuclear reactors. The doctoral student projects are supported by the funds from the APRI-12 program and Radiation Safety Authority (SSM).
The successful candidate is expected to (i) perform experimental and analytical studies on melt-structure interactions important to ex-vessel corium risk analysis of light water reactors; and (ii) develop models and perform validations for simulations for natural circulations in advanced water and lead cooled nuclear reactors. The U.S. NRC simulation codes, such as TRACE for thermal-hydraulics and severe accidents, are expected to be employed in the project of natural circulation.
Deadline :07.Oct.2024
About KTH Royal Institute of Technology, Stockholm, Sweden – Official Website
KTH Royal Institute of Technology, abbreviated KTH, is a public research university in Stockholm, Sweden. KTH conducts research and education within engineering and technology, and is Sweden’s largest technical university. Currently, KTH consists of five schools with four campuses in and around Stockholm.
KTH was established in 1827 as Teknologiska Institutet (Institute of Technology), and had its roots in Mekaniska skolan (School of Mechanics) that was established in 1798 in Stockholm. But the origin of KTH dates back to the predecessor to Mekaniska skolan, the Laboratorium Mechanicum, which was established in 1697 by Swedish scientist and innovator Christopher Polhem. Laboratorium Mechanicum combined education technology, a laboratory and an exhibition space for innovations. In 1877 KTH received its current name, Kungliga Tekniska högskolan (KTH Royal Institute of Technology). The King of Sweden Carl XVI Gustaf is the High Protector of KTH.
KTH is the highest ranked technical university in Sweden. It is ranked top 100 in the world among all universities in the 2020 QS World University Rankings.
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