Open positions

The proposed Ph.D. project is focused on developing mesoporous structures for application in nanofilters. The Si-based materials to be synthesized will be evaluated in terms of optoelectronic properties, ability to collect heavy metals from aqueous solutions, and antibacterial toxicity. The most promising materials will be employed for the fabrication of novel filters; the performance of assembled filtering systems will be conducted.

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Platform work constitutes the fastest growing segment of alternative work. While many problems (e.g. low wages, unfair treatment of workers) have been reported, the more existential question of worth has received scant attention. Other than providing an (extra) income and a flexible schedule, we know little of what makes platform work worth doing, and why. This is especially true for low-skill and low-status platform work such as ride-hailing or delivery work (often labelled as gig work). The goal of this research project, thus, is to explore why different types of platform work are considered work worth doing.

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The aim of the PhD project is to develop novel control methods for nonlinear dynamical systems with emphasis on energy and power systems domain. The developed methods will be based on combining the flatness-based feedforward control with the ideas from event-based control approach. There are different theoretical and practical aspects that the PhD candidate can study within the project.

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Utilization of waste streams can play a significant role in the cleaner production of building materials. Fly or bottom ashes, mining wastes, construction and demolition wastes etc. can be used for various purposes in practical applications, including fine and coarse aggregates for concrete in the construction industry or as a CO2 sink via mineral carbonation. The goal of the PhD research is to recycle locally available waste materials through accelerated carbonation into new and low CO2 emission building elements; namely low in cement, high in recycled wastes and chemically bound CO2. With this goal in mind, carbonation conditions of alkaline waste streams and fresh or hardened pre-cast products will be studied while exploring the use of these materials as cementitious mineral additives or as recycled aggregates into novel low-cement based composites. The expected changes in the chemical structure of the new cementitious paste because of the cementitious mineral additives and chemical reactions between CO2 and CO2 binding phases will be investigated focusing on the mechanical and durability properties of new building elements as well as their environmental characteristics.

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The Academy of Architecture and Urban Studies and the FinEst Centre for Smart Cities of Tallinn University of Technology (TalTech) invite applicants for a 4-year PhD position in the field of Urban Innovation and Smart City Development. The PhD candidate is expected to apply qualitative research methods to develop new insights into the governance mechanisms of sustainable smart city transitions, with a particular interest in the role played by the public sector. This PhD position is full-time and fully funded. The PhD position is part of this prestigious European project FinEst Twins, which has been instrumental in establishing the FinEst Centre for Smart Cities: a new Tallinn-based research and innovation organization that results from a joint venture between Tallinn University of Technology (Estonia), Aalto University (Finland), Forum Virium Helsinki (Finland) and two Ministries in Estonia. Hosted at TalTech, the FinEst Center for Smart Cities is boosting smart city research and translate scientific results into real-life innovations, by supporting the design, experimentation, and scale up of user-driven smart city solutions to urban challenges. The four-year PhD position at TalTech will make it possible to conduct research under the supervision of experienced professors and researchers working in the field of smart city transitions. The proposed project is highly international, and the successful candidate will have the possibility to engage with a broad network of leading universities and research centers which are already collaborating with the supervisory team. These collaborations include representatives of Massachusetts Institute of Technology, University College London, Copenhagen Business School, City University of Hong Kong, The University of Edinburgh, Stanford University, and Erasmus University Rotterdam, just to name a few.

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The PhD project aims at novel solutions for cross-layer reliability and self-health awareness for tomorrow’s intelligent autonomous systems and IoT edge devices. The enormous complexity of today’s advanced cyber-physical systems and systems of systems is multiplied by their heterogeneity and the emerging computing architectures employing AI-based autonomy. The researcher will study the dominant hardware reliability concerns, such as radiation-induced soft errors and nanoelectronics ageing specific to AI/ML hardware, processor architectures, such as RISC-V, and advanced FPGA SoCs.

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The PhD candidate shall develop a method that combines in-situ, hydrodynamic models and remote sensing data to identify and forecast patterns of the ocean dynamics (sea level, currents, waves etc.). This development requires: (i) bringing the various data sets to a common vertical reference datum using the geoid; (ii) using machine leaning methods to examine ocean dynamics especially with respect to patterns, spatial and temporal scales and inconsistencies amongst the data sources and (iii) validation (with independent data sources (e.g. field experiments) and sensitivity analysis (e.g. RMS error, and scatter index (SI), error budget) and (iv) using all the sources along with the relevant data on the contributors to perform statistical and machine learning algorithm to forecast the ocean dynamics observed and its associated uncertainty. The candidate is required to perform signal processing, statistical and computing techniques (in terms of RMS error, stand. dev, uncertainty estimates, error budgets, machine learning techniques etc.). From these results a specific model shall be developed that can forecast the sea level and current patterns that is essential for engineering and navigation purposes. The candidate is expected to assist in project related field campaigns.

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The PhD candidate shall develop a method that combines in-situ, hydrodynamic models and remote sensing data to develop continuous dynamic vertical reference. This development requires: (i) computing the sea level data from various sources and bringing them to a common vertical reference datum using the geoid; (ii) using machine leaning methods to merge the sea level and bathymetry data to enable adaptive routing and its error estimates; (iii) validation (with independent data sources (e.g. field experiments) and sensitivity analysis (e.g. RMS error, and scatter index (SI), error budget) and (iv) using all the sources (sea level, currents, bathymetry) along with the relevant data on the contributors to perform statistical and machine learning algorithm to and its associated uncertainty. The candidate is required to perform, statistical and computing techniques (in terms of RMS error, stand. dev, uncertainty estimates, error budgets, machine learning techniques etc.). From these results a specific model shall be developed that is essential for engineering and navigation purposes. The candidate is expected to assist in project related field campaigns.

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We are looking for a highly motivated and ambitious PhD candidate with background in electronics, laboratory automation, system integration, optics, or related fields to join our Microfluidics and Lab-on-a-chip team in Tallinn University of Technology (TalTech). The task of the PhD project is to develop an automated, in-line, chemical analysis platform that integrates flow control, light source(s), detector(s) and a single control interface. The aim of the automated platform is to integrate optical detection and flow control with a microfluidic system that separates chiral molecules.

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