Aalto University, Finland 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 Aalto University, Finland.
Eligible candidate may Apply as soon as possible.
(01) PhD Degree – Fully Funded
PhD position summary/title: Doctoral researcher for RF CMOS IC design
Are you passionate about RF integrated circuit design and eager to work on groundbreaking technologies? Look no further!
Our renowned research group, lead by professor Jussi Ryynänen and Sr. Scientist Kari Stadius, specializes in RF and millimeter-wave integrated circuit design and is at the forefront of innovation in 5G/6G communication, joint communication and sensing, and the co-design of antennas and transceiver electronics. We possess an exceptional design and measurement environment, complete with access to advanced FinFET and GaN IC technologies, providing an unparalleled platform for your research and development. By joining our group, you will receive hands-on education on CMOS design and gain invaluable experience, positioning yourself as an expert in high-frequency integrated circuit design.
Deadline : 31.10.2025
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in sustainable AI and cloud computing
The rapid growth of artificial intelligence (AI) has led to increased energy consumption in data centers, raising concerns about carbon emissions. To address these challenges and support sustainability goals, the project DecAI aims to decarbonize AI through long-term impact assessment and optimization. The goal is to develop a framework to estimate carbon emissions across AI’s development, operation, and use. This framework enables stakeholders, such as AI providers and data center operators, to evaluate and compare scenarios to reduce emissions. The framework will be released as a software tool to guide emissions reduction strategies. The project aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits for the electricity grid. By addressing AI’s entire lifecycle, DecAI offers a holistic approach to reducing carbon emissions.
Deadline : 31.10.2025
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral researcher for RF CMOS IC design
Are you passionate about RF integrated circuit design and eager to work on groundbreaking technologies? Look no further!
Our renowned research group, lead by professor Jussi Ryynänen and Sr. Scientist Kari Stadius, specializes in RF and millimeter-wave integrated circuit design and is at the forefront of innovation in 5G/6G communication, joint communication and sensing, and the co-design of antennas and transceiver electronics. We possess an exceptional design and measurement environment, complete with access to advanced FinFET and GaN IC technologies, providing an unparalleled platform for your research and development. By joining our group, you will receive hands-on education on CMOS design and gain invaluable experience, positioning yourself as an expert in high-frequency integrated circuit design.
Deadline : October 31th, 2025
(04) PhD Degree – Fully Funded
PhD position summary/title: Doctoral researcher for assessing carbon flows in the built environment
Are you interested in circular economy and life cycle assessment in architecture and construction? Do you want to work for turning down the emissions and overuse of materials in the built environment?
We are now looking for a doctoral candidate for a PhD position at Aalto University’s Department of Architecture. In this position you will have a chance to make an impact by analysing and developing solutions for effectively capturing carbon into the built environment. Join us in shaping the future!
The position is part of Profi 8 research “Transformation of urban systems” funded by the Research Council of Finland. The work aims at modeling carbon flows, developing solutions for removing and storing greenhouse gases into the built environment, and developing the life cycle assessment method for quantifying multiple cycles of using building products.
Deadline : 20th of October, 2025
(05) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in Sustainable Renewable Energy Engineering, Modeling and Optimization
Your main objective is to develop a simulation and optimization model of a large hydrogen valley operating in off-grid mode (having also an option for grid connection). As outputs the model should give sustainable and optimal capacities of system components (wind power plants, electrolyzers, P2X plants, data centers, transmission system for power H2, CO2 and heat, energy storages, etc.) and their optimal locations. Our aim is to maximize the sustainable production capacity of green hydrogen and e-fuels within the studied area. The model needs to consider variability of weather conditions and price variation of electricity and district heat. The solution is restricted by biodiversity loss, social acceptability and cultural boundary conditions, defined together with the research team of energy engineers, social scientists, ecologists, and landscape architects. Our ultimate research question is: How much green hydrogen and e-fuels can be produced on sustainable level within a defined area, and can we still have a competitive selling price of products?
Deadline : 15th of October 2025
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(06) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in two-dimensional (2D) Nanodevices and Quantum Materials
The goal of the doctoral researcher is to study emergent electronic phenomena in two-dimensional materials by gate-tunable scanning tunneling microscopy (STM). Typical examples of these materials include van der Waals monolayers of transition metal dichalcogenide superconductors, Mott insulators, ferromagnets, multiferroics, etc.
Deadline : 10th October 2025
(07) PhD Degree – Fully Funded
PhD position summary/title: Doctoral researcher, lignocellulose chemistry
The doctoral researcher will be working in a project investigating metal cation coordination with cellulose for fiber functionalization The aim for the doctoral researcher is to become an expert in lignocellulose chemistry with a twist of inorganix chemistry.
Deadline : October 10th, 2025
(08) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in Multi-Perspectives Analysis of Catalytic Hydrogenation of Carbon Oxides to Oxygenates as Green Energy Carriers
We are now looking for one talented and highly motivated Doctoral Researcher in catalytic conversion technologies to be working at the Department of Energy and Mechanical Engineering and Department of Chemical and Metallurgical Engineering
We are working on alternative catalytic (homogenously and heterogeneously catalyzed) hydrogenation technologies targeting valorization of carbon oxides for green oxygenates production. In this project, we particularly aim green methanol production in the context of circular economy.
The research activities in this doctoral thesis are planned with the view of developing a comprehensive multi-perspectives analysis approach, essential to track the impacts of the characteristics in different scales of the investigated processes on their overall techno-economic-environmental-energy efficiency. This is an interdisciplinary project requiring expertise in both homogenous and heterogenous catalytic conversion, including the handling, testing and characterizing the catalysts as well as the reactor and process simulation and analysis. Details will be adjusted collaboratively as the project progresses. The project has access to two experimental reactor setups, common equipment as well as characterization techniques at university scale.
Deadline : September 30th, 2025
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(09) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher and Postdoc positions to work on Computational Social Intrinsic Motivation (SIM)
Our goal is to advance social AI, i.e. artificial agents capable of supporting us proactively or reactively in a range of virtual or embodied tasks. However, existing social AI and artificial agents more generally still struggle in delivering reliable support in situations that were not anticipated at design-time, and their decision-making processes may be hard to understand by interaction partners or operators. This project tackles these challenges by advancing Social Intrinsic Motivation (SIM), i.e. computational models of intrinsic motivation applied to the interaction of multiple agents.
Intrinsic motivation (IM), with curiosity being a well-known example, is crucial to human development, allowing us to acquire new knowledge and skills in the absence of specific tasks, and to adapt to new situations. Computational models of IM [*] inherit these properties, making artificial agents more independent of external instruction and allowing them to perform better in previously unknown tasks. Typically formalised in the framework of reinforcement learning, computational models of IM are not black boxes, which enables explanation of the emerging behaviour and supports trust by the AI’s designers and interaction partners.
SIM seeks to leverage the power of IM in the interaction of multiple agents. One example could be motivating an AI to optimise their partner’s intrinsic motivation, thus empowering them through its own behaviour. Pioneering work led to exciting findings such as emergent support of human interaction partners across tasks and without external instruction [1-4] or considerably improved cooperation within societies of artificial agents [5,6]. These are only few examples and what defines SIM is an open question – to be addressed in this project.
Deadline : 30.9.2025
(10) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in AI-Enhanced Adaptive Design for Dynamic Landscapes
The Adaptive Design for AI-Driven Processes in Transforming Dynamic Landscapes (ADAPT) project develops scalable, data-driven design methodologies that transform dynamic environmental processes, such as sedimentation, meltwater flow, and vegetation change, into active drivers of adaptive design. This interdisciplinary work combines advanced computational tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier environments.
You will collaborate closely with world-leading partners including ETH Zurich, MIT Media Lab, and the Norwegian University of Life Sciences (NMBU), contributing to research that redefines how we design for climate-driven environmental change.
Deadline : 30 September 2025
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(11) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher positions in Computational Physics
Project 1: Autonomous, interpretable and precise nanofabrication in scanning probe microscopy
- Academy Research Fellow: Nian Wu (Google Scholar)
- Research mobility: University of Cambridge (UK), Universidad Autonoma de Madrid (Spain), Peking University (China), National University of Singapore (Singapore), National Institute for Materials Science (Japan).
- Brief introduction: The goal of this project is to develop autonomous and interpretable approaches capable of precisely steering chemical reactions and assembling functional nanomaterials from bottom-up in scanning tunnelling microscopy (STM). The project is tightly linked to machine learning algorithms in images (image classifier, image segmentation) or machine learning interatomic potentials (MLIPs), or reinforcement learning algorithms in decision-making. Experience in SPM experiments is a bonus, but not essential.
Project 2: Structural machine learning for high resolution tip-enhanced Raman spectroscopy
- Academy Research Fellow: Orlando José Silveira (Google Scholar)
- Research Mobility: Max Plank Institute for the structure and dynamics of matter (Germany)
- Brief Introduction: We are looking for a motivated PhD student to join our team at Aalto University to develop new methods for molecular imaging at atomic resolution. The project focuses on combining Tip-Enhanced Raman Spectroscopy (TERS) with artificial intelligence to enable automated structure discovery of organic molecules. By training machine learning models on datasets generated from density functional theory simulations, we aim to interpret complex TERS images and push the limits of nanoscale optical imaging. The student will develop and implement new theoretical methodologies and apply machine learning to analyse and interpret TERS images.
Project 3: Designer magnetic molecular systems on 2D van der Waals layers
- Academy Research Fellow: Nan Cao (Google Scholar)
- Research Mobility: Donostia International Physics Center (Donostia-San Sebastián, Spain), IBS Center for Quantum Nanoscience (Seoul, Korea), Max Planck Institute for Chemical Physics of Solids (Dresden, Germany).
- Brief Introduction: The project explores low-dimensional molecular magnetic systems using an integrated approach, including theory (first-principles simulations, model Hamiltonians), data-driven methods and experiments (on-surface synthesis). It aims to design π-conjugated molecular spin systems with exotic electronic and magnetic properties and accelerate quantum material discovery using machine learning. Candidates with machine learning expertise are especially encouraged to apply, as data-driven methods will play an increasingly important role in the project. Experience in quantum magnetism is also highly valued.
Deadline : 30th September 2025
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(12) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Researcher in nonlinear systems and control
Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control.
The main task of the PhD student will be to develop sound data-driven methodologies for learning control policies with provable guarantees on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent developments in learning Operator Theoretic representations of dynamical systems that focus on model interpretability, scalability to high dimensions, and data efficiency. The exact direction of the research is chosen depending on your experience and interests. Please relate clearly to some of the research topics in your Letter of Motivation.
Outstanding researchers from the areas of Control Engineering, Robotics, Machine Learning, AI, and related areas including Optimization, Mathematics and Physics are welcome to apply. The candidate is expected to conduct independent research and should have strong analytical skills as well as be fluent in spoken and written English. Successful candidates will have the opportunity to collaborate with the vibrant Aalto research community, including the Intelligent Robotics group and the Mobile Robotics group, as well as the Finnish Center for Artificial Intelligence. The group also actively collaborates internationally with top institutions in the US and Sweden.
Deadline : September 29th 2025
(13) PhD Degree – Fully Funded
PhD position summary/title: Doctoral researcher in Catalytic Advanced Oxidation Processes (AOPs)
If you are a passionate Scientist/Engineer about the emerging challenges facing water treatment industry particularly emerging micropollutants and have the motivation to take part in developing innovative solutions for such challenges, this position is a good fit for you.
In this role, you will be working with Assistant Prof. Raed Al-Juboori in the Water and Wastewater Research Group within the Department of Built Environment at the School of Engineering. This dynamic group stands out for its unique approach—combining rigorous fundamental research with practical, real-world applications. With strong ties to the Finnish water and wastewater industry, the group has successfully led numerous high-impact projects in collaboration with partners across Europe and around the world.
The selected candidate will be working on the development of catalytic hybrid AOP reactor and evaluate the combination of selected technologies for the removal of prominent pharmaceutical contaminants in Finnish water. The work will also include developing effective scalable catalyst design and studying metabolites and their toxicity.
Deadline : September 29, 2025
About Aalto University, Finland –Official Website
Aalto University is a university located in Espoo, Finland. It was established in 2010 as a merger of three major Finnish universities: the Helsinki University of Technology (established 1849), the Helsinki School of Economics (established 1904), and the University of Art and Design Helsinki (established 1871). The close collaboration between the scientific, business and arts communities is intended to foster multi-disciplinary education and research. The Finnish government, in 2010, set out to create a university that fosters innovation, merging the three institutions into one.
The university is composed of six schools with close to 17,500 students and 4,000 staff members, making it Finland’s second largest university. The main campus of Aalto University is located in Otaniemi, Espoo. Aalto University Executive Education operates in the district of Töölö, Helsinki. In addition to the Greater Helsinki area, the university also operates its Bachelor’s Programme in International Business in Mikkeli and the Metsähovi Radio Observatory in Kirkkonummi.
Aalto University’s operations showcase Finland’s experiment in higher education. The Aalto Design Factory, Aalto Ventures Program and Aalto Entrepreneurship Society (Aaltoes), among others, drive the university’s mission for a radical shift towards multidisciplinary learning and have contributed substantially to the emergence of Helsinki as a hotbed for startups.Aaltoes is Europe’s largest and most active student run entrepreneurship community that has founded major concepts such as the Startup Sauna accelerator program and the Slush startup event.
The university is named in honour of Alvar Aalto, a prominent Finnish architect, designer and alumnus of the former Helsinki University of Technology, who was also instrumental in designing a large part of the university’s main campus in Otaniemi.
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