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 Candidate in Integrated Circuit Design for Wireless Applications
In this position, you will do research and doctoral studies on integrated electronic circuit design. Your work will be related to our research projects focusing on wireless IC design, particularly for emerging 5/6G communication. You will learn to design integrated circuits applying the latest nanometer-scale CMOS technology. Depending on your interest and background we can offer you a research topic ranging from MMIC design to mixed-mode and digital designs. You will join an active research group, which research is closely linked to our industrial and academic partners. Our target is that you will acquire the doctoral degree in four years.
Deadline : 28.2.2022
(02) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Candidate and Postdoc Positions on Magnonic/Plasmonic Devices, Magnonic Neural Networks, and Plasmonic Condensates/Nanolasers
We are looking for highly motivated doctoral candidates and postdoctoral researchers to work on experiments in three projects:
1) optical control of spin waves for low-power computing,
2) YIG-based magnonic neural networks, and
3) plasmonic nanolasers and Bose-Einstein condensates (BEC) in flat and/or topological bands.
Deadline : 28.2.2022
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(03) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Candidate to join the Computational Chemistry group
In this position, you will have a chance to be a part of research project that is focused on developing explainable artificial intelligence (XAI) based tools to accelerate the development of redox flow battery (RFB) chemistries. Specifically, we will develop DFT based computational screening and machine learning (ML) tools to facilitate rapid evaluation of RFB materials to identify the most promising candidates. We will use new Explainable Artificial Intelligence (XAI) methods to rationalize the vast amount of computational data. With DFT methods we can compute the structures and properties of tens of thousands of molecules, and XAI will then enable us to pinpoint the important molecular features contributing to the redox potential and other properties such as stability and reactivity. Armed with this information, we can design selected sets of new promising molecules and evaluate their properties more rigorously with computational methods. The computational group will work in close collaboration with flow battery group in University of Turku (Prof. Peljo) and organic chemistry group in University of Jyväskylä (prof. Pihko).
Deadline : 15.2.2022
(04) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Explainable AI for Systems Biomedicine
In this project, we aim to develop AI technologies to help knowledge discovery in big molecular biology data, with applications to improve diagnoses and personalized treatments of complex diseases. We will extend our recently developed technology for learning sparse non-linear models, to the analysis of multi-view network data (Huusari et al., 2021), and develop a capability of embedding prior knowledge for explainability. In applications, we will focus on the analysis of metagenomic data arising from the human gut microbiome (Ravikrishnan and Raman, 2021), which is known to have a significant role in many diseases such as irritable bowel syndrome and diabetes. The results are expected to give new knowledge on the role of the gut microbiome in these diseases, explained in terms of key metabolic signatures, and thus enable improved diagnosis and treatment. In machine learning, the methods extend the state of the art in learning from complex multi-view and network data.
Deadline : 6.2.2022
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(05) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Civic Agency in AI? Democratizing Algorithmic Services in the City (CAAI)
The public sector is increasingly embracing algorithmic decision-making and data-centric infrastructures to offer innovative digital services to citizens. The CAAI project conducts research to assess wide ranging and conflicting perspectives and practices among experts, providers and citizens. The European Commission’s proposed Artificial Intelligence Act has raised vigorous deliberations regarding the implications of implementing this regulatory framework across the EU. The diverse and contested discourses among AI experts, regulators, public actors, and citizen advocates, offer a timely window of opportunity to critically examine their implications, while promoting citizen participation and civic agency in shaping the AI Act and its governance in Finland and the EU. In this project we examine discourses through the theoretical lens of Critical Discourse Analysis to gain insights on diverse values, narratives, and positions, while Natural Language Processing methods are used to contextually examine salient topics.
Deadline : 6.2.2022
(06) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Virtual Laboratories: Drug Design
We develop modeling methods for drug design, both generative models of the drug molecules and their effects, and collaborative AI methods for assisting the drug designers in their task. The idea is to help experts steer the modeling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving.
Deadline : 6.2.2022
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(07) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for Human-AI collaboration
The Cognitive computing research group is seeking 1-2 PhD candidates to conduct research in Machine learning for Human-AI collaboration. The candidate will work on machine learning methods that allow new types of man-machine interactions in which machines can learn directly from human behaviour and physiology. The methods will be developed and applied for physiological sensing and brain-computer interfacing data. The position provides an opportunity to contribute both to fundamental computer science and ground-breaking human-computer interaction research. The candidate will join world—class research environment and will be part of the Cognitive computing research group (https://www.cs.helsinki.fi/group/intercom/). The starting date is negotiable, but the positions are filled when suitable candidates are found.
Deadline : 6.2.2022
(08) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Usable and accessible eHealth Services
We are looking for a PhD student who is interested in the health field and wants to work on the societally important research topic. Familiarity with the health field, user-centered design, and statistical analysis methods is preferred.
Deadline : 6.2.2022
(09) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for collaborative AI
A prime goal for FCAI’s research is to develop a new form of AI that can better work with people and assist them in everyday tasks. This can be seen as a probabilistic modeling task which requires data-efficient inference on multi-agent models, and some prior knowledge from cognitive science. We are now looking for an outstanding machine learning researcher who wants to develop with us the theory and inference methods for this new task. This will involve multi-agent modeling, POMDPs and reinforcement learning, and inverse reinforcement learning.
Deadline : 6.2.2022
(10) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Deep generative modeling for 1) precision medicine and 2) continuous-time dynamical systems
Project-1:
We are looking for a doctoral student to develop novel probabilistic machine learning methods for large-scale health datasets from biobanks, clinical trials and/or single-cell sequencing experiments. This project develops novel deep generative modeling methods to (i) predict adverse drug effects using longitudinal/time-series data from large-scale biobanks and clinical trials, and to (ii) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trials. Methodologically this project includes e.g. VAEs, GANs, Bayesian NNs, domain adaptation, Gaussian processes and causal analysis. The work will be done in collaboration with research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique real-world data sets from our collaborators in university hospitals and big pharma company.
Project-2:
Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs [1] and neural ODEs. These methods are particularly useful in learning arbitrary continuous-time dynamics from data, either directly in the data space [1] or in a latent space in case of very high-dimensional data [3]. We are looking for a doctoral student to join our current efforts to (i) develop efficient yet calibrated Bayesian methods for learning such black-box ODE models, (ii) develop neural ODEs to learn arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning and causal analysis.
Deadline : 6.2.2022
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(11) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Sustainable ICT
The goals of this project are to understand the reasons why the use of ICT is growing and what can be done to lower the direct impact of the ICT sector on our environment. Growth of ICT can be attributed to new services but also to how services are built, and how little optimisations are done to make these digital services more resource aware.
Deadline : 6.2.2022
(12) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Compressed Indexing for Pangenomics and Genomic Epidemiology
This project will develop the next generation of indexing data structures that allow fast and scalable search and update of pangenomes. The aim is to design memory-efficient compressed data structures that are simultaneously able to store and enable rapid search over the genomic data that makes up the pangenome, and to design accompanying index construction algorithms capable of scaling to terabytes of sequence data. The project draws on the world-leading expertise in compressed data structures of the PI’s group, as well as that of the wider Algorithmic Bioinformatics research cluster at University of Helsinki.
Deadline : 6.2.2022
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(13) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in AI algorithms for quantitative biology
The ideal candidate will have experience in machine learning methods e.g. reinforcement learning and symbolic regression. However, as our research is cross-disciplinary it is possible to contribute coming from several different fields. Thus, we welcome applications from exceptional candidates, with a highly quantitative background, from other fields.
Deadline : 6.2.2022
(14) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Reconstructing Crisis Narratives for Trustworthy Communication and Cooperative Agency
Join a collaborative team of PhDs, Postdocs and academic researchers working on a project jointly conducted between Aalto University and the Finnish Institute for Health and Welfare (THL). The project seeks to analyze and reconstruct crisis narratives using mixed-methods, combining qualitative research for narrative inquiry with computational data analytics of crisis discourses in news and social media to understand global pandemics. Candidates will work at the intersection of Human-Computer Interaction (HCI), design research, computational social sciences, and public health for critical societal impact. We expect the candidates to have backgrounds in computer science and/or the social sciences.
Deadline : 6.2.2022
(15) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning in precision oncology
We are looking for PhD students with BSc/MSc level education in data science, machine learning or mathematical modeling, and interest in applying computational methods to cancer research. Basic knowledge of cancer biology or genetics is a plus but not required. The possible PhD projects range from developing computationally effective methods for very large datasets to modeling tumor evolution and integration multi-omics cancer data.
Deadline : 6.2.2022
(16) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Eco-Evolutionary Control Theory
The ideal candidate will have experience in reinforcement learning, stochastic optimal control theory and evolutionary theory. However, as our research is cross-disciplinary it is possible to contribute coming from several different fields. Thus, we welcome applications from exceptional candidates, with a highly quantitative background, from other fields.
Deadline : 6.2.2022
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(17) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine Learning for Health (ML4H)
Project description: Recent years have witnessed accumulation of massive amounts of health data, enabling researchers to address a range questions such as: how to allocate healthcare resources fairly and efficiently, how to provide personalized guidance and treatment to users based on real-time data from wearable devices, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include integrating noisy data from heterogeneous data sources, going beyond correlation to learn about causality, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles:
Deadline : 6.2.2022
(18) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Explainable AI for Systems Biomedicine
In this project, we aim to develop AI technologies to help knowledge discovery in big molecular biology data, with applications to improve diagnoses and personalized treatments of complex diseases. We will extend our recently developed technology for learning sparse non-linear models, to the analysis of multi-view network data (Huusari et al., 2021), and develop a capability of embedding prior knowledge for explainability. In applications, we will focus on the analysis of metagenomic data arising from the human gut microbiome (Ravikrishnan and Raman, 2021), which is known to have a significant role in many diseases such as irritable bowel syndrome and diabetes. The results are expected to give new knowledge on the role of the gut microbiome in these diseases, explained in terms of key metabolic signatures, and thus enable improved diagnosis and treatment. In machine learning, the methods extend the state of the art in learning from complex multi-view and network data.
Deadline : 6.2.2022
(19) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Civic Agency in AI? Democratizing Algorithmic Services in the City (CAAI)
Applicants must show a keen interest and background research in topics related to this project including AI ethics, digital citizenship, human computer interaction, participatory design, algorithmic literacy, AI governance, critical discourse analysis, and/or natural language processing.
Deadline : 6.2.2022
(20) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Virtual Laboratories: Drug Design
We develop modeling methods for drug design, both generative models of the drug molecules and their effects, and collaborative AI methods for assisting the drug designers in their task. The idea is to help experts steer the modeling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving.
Deadline : 6.2.2022
(21) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for Human-AI collaboration
The Cognitive computing research group is seeking 1-2 PhD candidates to conduct research in Machine learning for Human-AI collaboration. The candidate will work on machine learning methods that allow new types of man-machine interactions in which machines can learn directly from human behaviour and physiology. The methods will be developed and applied for physiological sensing and brain-computer interfacing data. The position provides an opportunity to contribute both to fundamental computer science and ground-breaking human-computer interaction research. The candidate will join world—class research environment and will be part of the Cognitive computing research group (https://www.cs.helsinki.fi/group/intercom/). The starting date is negotiable, but the positions are filled when suitable candidates are found.
Deadline : 6.2.2022
(22) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Usable and accessible eHealth Services
eHealth services use emerging information technology to support wellbeing, health, and healthcare. Finland is a forerunner in the digitalization of healthcare with an increasing number of national eHealth services such as Omakanta, Omaolo, and Health Village. The goal is to encourage people to be active in taking care of their health. However, eHealth services are least used by persons who need them most and not everyone can access and use the services. In two research projects (www.digiin.fi and www.nordehealth.eu), we aim to 1) support the development of usable and acceptable eHealth services to people who are most vulnerable and maybe least interested in health and 2) benchmark national patient portals with access to electronic health in Finland, Sweden, Norway, and Estonia.
Deadline : 6.2.2022
(23) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for collaborative AI
A prime goal for FCAI’s research is to develop a new form of AI that can better work with people and assist them in everyday tasks. This can be seen as a probabilistic modeling task which requires data-efficient inference on multi-agent models, and some prior knowledge from cognitive science. We are now looking for an outstanding machine learning researcher who wants to develop with us the theory and inference methods for this new task. This will involve multi-agent modeling, POMDPs and reinforcement learning, and inverse reinforcement learning.
Deadline : 6.2.2022
(24) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Deep generative modeling for 1) precision medicine and 2) continuous-time dynamical systems
Project-1:
We are looking for a doctoral student to develop novel probabilistic machine learning methods for large-scale health datasets from biobanks, clinical trials and/or single-cell sequencing experiments. This project develops novel deep generative modeling methods to (i) predict adverse drug effects using longitudinal/time-series data from large-scale biobanks and clinical trials, and to (ii) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trials. Methodologically this project includes e.g. VAEs, GANs, Bayesian NNs, domain adaptation, Gaussian processes and causal analysis. The work will be done in collaboration with research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique real-world data sets from our collaborators in university hospitals and big pharma company.
Project-2:
Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs [1] and neural ODEs. These methods are particularly useful in learning arbitrary continuous-time dynamics from data, either directly in the data space [1] or in a latent space in case of very high-dimensional data [3]. We are looking for a doctoral student to join our current efforts to (i) develop efficient yet calibrated Bayesian methods for learning such black-box ODE models, (ii) develop neural ODEs to learn arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning and causal analysis.
Deadline : 6.2.2022
(25) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Sustainable ICT
ICT has had tremendous effect on our world, bringing new kids of services and enhancing old ones. In terms of sustainability, ICT helps, for example, to reduce green house gas emissions in other sectors, digital solutions support environmental protection and adaptation to climate change. Yet, at the same time the use of ICT is growing at a huge pace. The traffic on the Internet and mobile networks grows year after year, and new and larger data centres are built all over the world. At the same time the performance of ICT hardware has increased extremely fast. As an example, a smart phone today has the same computing performance as the super computers in the 90’s. ICT services consume increasing amounts of energy and natural resources are needed for building new devices.
Deadline : 6.2.2022
(26) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Compressed Indexing for Pangenomics and Genomic Epidemiology
A pangenome is a catalog of all the genetic variation – single base changes, structural variants, regulatory regions and genes – found within a population or species. Pangenomes are revolutionizing biology, providing new insights into evolution and biodiversity. For example, recent breakthroughs in population genomics have demonstrated that mapping and understanding variation within a species (represented by its pangenome) is becoming essential for most high-impact applications, such as biomarker discovery, pathogen surveillance, modeling tumor evolution and prediction of treatment outcomes.
Deadline : 6.2.2022
(27) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Civic Agency in AI? Democratizing Algorithmic Services in the City (CAAI)
Applicants must show a keen interest and background research in topics related to this project including AI ethics, digital citizenship, human computer interaction, participatory design, algorithmic literacy, AI governance, critical discourse analysis, and/or natural language processing.
Deadline : 6.2.2022
(28) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Virtual Laboratories: Drug Design
We develop modeling methods for drug design, both generative models of the drug molecules and their effects, and collaborative AI methods for assisting the drug designers in their task. The idea is to help experts steer the modeling system towards their design goals, while eliciting their prior knowledge to improve the models of the drugs. This is difficult because the goals may be tacit, uncertain and evolving.
Deadline : 6.2.2022
(29) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for Human-AI collaboration
The Cognitive computing research group is seeking 1-2 PhD candidates to conduct research in Machine learning for Human-AI collaboration. The candidate will work on machine learning methods that allow new types of man-machine interactions in which machines can learn directly from human behaviour and physiology. The methods will be developed and applied for physiological sensing and brain-computer interfacing data. The position provides an opportunity to contribute both to fundamental computer science and ground-breaking human-computer interaction research.
Deadline : 6.2.2022
(30) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Usable and accessible eHealth Services
We are looking for a PhD student who is interested in the health field and wants to work on the societally important research topic. Familiarity with the health field, user-centered design, and statistical analysis methods is preferred.
Deadline : 6.2.2022
(31) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Machine learning for collaborative AI
A prime goal for FCAI’s research is to develop a new form of AI that can better work with people and assist them in everyday tasks. This can be seen as a probabilistic modeling task which requires data-efficient inference on multi-agent models, and some prior knowledge from cognitive science. We are now looking for an outstanding machine learning researcher who wants to develop with us the theory and inference methods for this new task. This will involve multi-agent modeling, POMDPs and reinforcement learning, and inverse reinforcement learning.
Deadline : 6.2.2022
(32) PhD Degree – Fully Funded
PhD position summary/title: HICT: Doctoral Candidate in Deep generative modeling for 1) precision medicine and 2) continuous-time dynamical systems
Project-1:
We are looking for a doctoral student to develop novel probabilistic machine learning methods for large-scale health datasets from biobanks, clinical trials and/or single-cell sequencing experiments. This project develops novel deep generative modeling methods to (i) predict adverse drug effects using longitudinal/time-series data from large-scale biobanks and clinical trials, and to (ii) harmonize large-scale health data sets for AI-assisted decision making to revolutionize future clinical trials. Methodologically this project includes e.g. VAEs, GANs, Bayesian NNs, domain adaptation, Gaussian processes and causal analysis. The work will be done in collaboration with research groups from the Finnish Center for Artificial Intelligence, and the novel methods will be tested using unique real-world data sets from our collaborators in university hospitals and big pharma company.
Project-2:
Recent machine learning breakthroughs include black-box modeling methods for differential equations, such as Gaussian process ODEs [1] and neural ODEs. These methods are particularly useful in learning arbitrary continuous-time dynamics from data, either directly in the data space [1] or in a latent space in case of very high-dimensional data [3]. We are looking for a doctoral student to join our current efforts to (i) develop efficient yet calibrated Bayesian methods for learning such black-box ODE models, (ii) develop neural ODEs to learn arbitrary dynamics of high-dimensional systems (e.g. in robotics, biology, physics or video applications) using a low-dimensional latent space representation, and (iii) further developing these methods for reinforcement learning and causal analysis.
Deadline : 6.2.2022
(33) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Candidate in the area of Materials Science for Lithium-ion Batteries Recycling
We are looking for a highly motivated PhD candidate to join a multidisciplinary initiative at the forefront of the circular economy of raw materials. In this research position at the SmartCycling project, you will be developing new ways to analyze the characteristics of battery materials to help with the design of more efficient recycling processes following the principles of the circular economy. In the SmartCycling project, you will work closely with collaborators in the field of machine learning and artificial intelligence to find new approaches to process design.
Deadline : 31.1.2022
(34) PhD Degree – Fully Funded
PhD position summary/title: Doctoral candidates in Quantum Nanomechanics
In another project, the goal is to touch a hundred-year-old mystery of physics: Despite its success at describing phenomena in the low-energy limit, quantum mechanics is incompatible with general relativity that describes gravity and huge energies. The interface between these two has remained experimentally elusive, because only the most violent events in the universe have been considered to produce measurable effects due to the plausible quantum behavior of gravity. We aim at detecting gravitational forces for the first time within a quantum system. We use mechanical oscillators loaded by milligram masses, and bring two such gravitationally interacting oscillators into nonclassical motional states. Initially, we measure the gravitational force between gold particles weighing a milligram, representing a new mass scale showing gravitational forces within a system.
Deadline : 31.1.2022
(35) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Candidate in Drinking Water Quality and Treatment
Aalto Water and Environmental Engineering Research Group (Water and Environmental Engineering | Aalto University), namely prof. Riku Vahala, has a vacancy for a doctoral candidate (“PhD student”) in a research project dealing with characterization and removal of natural organic matter (NOM) from drinking water. NOM has several adverse effects, such as providing substrate for microbial growth in the drinking water distribution system and acting as a precursor for disinfection by-products. The overall goal of the project is to better understand, which NOM fractions need to be removed from drinking water and how they can be characterized. The project is carried out in close collaboration with Helsinki Region Environmental Services, HSY, who is providing municipal water supply and waste management services in the Helsinki metropolitan area.
Deadline : 31.1.2022
(36) PhD Degree – Fully Funded
PhD position summary/title: Doctoral Candidate in Wastewater Network Asset Management
Aalto Water and Environmental Engineering Research Group (Water and Environmental Engineering | Aalto University), namely prof. Riku Vahala, has a vacancy for a doctoral candidate (“PhD student”) to work in the WATCON research project dealing with data-driven assessment and management of wastewater network assets. Making informed decisions on condition inspections and renovations is relevant, since wastewater networks are critical infrastructure and ageing and thus require large investments. The goal of the project is to improve wastewater network condition management based on the analysis of condition data derived from CCTV inspections. The project is carried out in collaboration with UOulu, whose research concentrates on inflow and infiltration quantification using multi-isotope analysis.
Deadline : 31.1.2022
(37) PhD Degree – Fully Funded
PhD position summary/title: Doctoral candidate position in the area of Cyber Trust, Security and Privacy
You will conduct research and doctoral studies in the area of IoT and Wireless Security in an independent way with supervision. Alternatively, your research topic may be focused to wireless security, physical security, machine learning, trusted computing, privacy preservation, data analytics, positioning system. You will be working in an international and cross-disciplinary team lead by Dr. Zheng Yan (https://people.aalto.fi/zheng.yan).
Deadline : 30.1.2022
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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