The main motivation for young researchers is the desire to understand complex things and help people around to comprehend them. It is a desire to contribute to science and the development of new technologies. Five first year doctoral students of the University of Latvia (UL) in the field of natural sciences have received a scholarship from the patron “Mikrotikls” Ltd. for the implementation of their research ambitions.

When evaluating the applicants, such aspects as the uniqueness of the research, the environment of the researcher, including the equipment, the content of the application and the credibility of the realization, the academic maturity (previous scientific activity) were taken into account.

Scholarship of the patron “Mikrotikls” Ltd. for the UL first year doctoral students in the field of natural sciences, administered by the UL Foundation, is awarded for the second year. Last year, six doctoral students received it. The amount of each scholarship is 1000 euros per month. Succesfuly fulfilling set goals, the researchers will receive the scholarship for three years, during which they will develop their doctoral work.

Didzis Berenis: Numerical simulation of rotating permanent magnet induced turbulent liquid metal flow

UL Faculty of Physics, Mathematics and Optometry. Thesis advisor: Dr.phys. Ilmars Grants

In metal processing industry the melted metal in furnaces is mixed to ensure similar temperature rise throughout the liquid metal volume and traditionally it is done with mechanical mixers. Alternatively, mixing can be performed in a contactless way, this way prolonging lifetime of the mixer. In the Laboratory of MHD Technology at the Physics Institute of the University of Latvia are developed permanent rotating magnet mixers with 10 times the performance of electromagnetic mixers and are recognized in industry as a better alternative. During the research, the rotating permanent magnet induced liquid metal flow properties in large – industrial sized containers, where it will be calculated and described qualitatively and quantitatively. The results of the research will enable the use of the Physics Institute’s developed methods in various other industrial processes and devices. The aim of the research is to develop theoretical basis for industrial rotating permanent magnet liquid metal mixer development with predictable results and find the device parameters for the optimal working conditions.

Martins Kalis: Quantum machine learning algorithms for noisy intermediate-scale and universal quantum computers

UL Faculty of Computing. Thesis advisor: prof. Andris Ambainis

Classical computing resources are beginning to become restrictive in machine learning and other areas, resulting in a strong demand for alternative solutions. Currently, quantum computing is the only computational model that can provide exponential acceleration in solving some problems compared to classical computers. Only in 2016 the first quantum computer prototypes became available to the general public. They can be easily simulated even on a personal computer. However, several companies have already announced that they are planning to create quantum computers in the nearest future, which will be able to solve certain problems that cannot be solved with classical computers.

The development of algorithms for quantum computers is challenging. In order to create an algorithm that is more efficient than the classical algorithm, it is necessary to use quantum effects that are largely unintuitive to a mind trained in classical physics. And even though a device with 10 quantum bits internally can simultaneously perform operations on 1024 (2^10) different binary numbers, only one such number can be extracted through a measurement. However, the potential benefits of quantum computers are large enough for quantum computing and quantum machine learning to be an active research area globally. A fortunate side-effect of the development of quantum computing is the transfer of new ideas to classical computing. This work will examine the possibilities of using both universal and noisy intermediate-size quantum computers in the field of machine learning.


Linards Klavins: Valorization of berry press residues of Vaccinium species, extraction using “green” technologies and composition of biologically active substances

UL Faculty of Geography and Earth Sciences, Environmental sciences. Thesis advisor: prof. Dr. chem. Arturs Viksna

The aim of this study is to investigate commercially grown and wild berries (bilberries, bog bilberries, blueberries, lingonberries, bog cranberries, American cranberries, all belonging to Vaccinium spp.) for their biologically active substances and possible ways to extract these substances using environment-friendly methods. Berries and their press residues are rich with substances that have positive effect on human health, for example, lipids and polyphenolics. Polyphenolics that are found in berries act as potent antioxidants in the human body, lowering the risks that are caused by oxidative stress. In this study the contents and composition of biologically active substances will be investigated in order to provide information about possibilities to implement these substances into functional foods, cosmetics and nutritional products. To achieve the set goals encapsulation methods combined with product purification and drying methods used in food industry will be employed.


Karlis Pleiko: Target protein identification for renal carcinoma specific aptamers

UL Faculty of Biology, Molecular biology. Thesis advisor: prof. Una Riekstina.

Renal tumors make up 2-3% of all tumors in the adult population. The most common subtype is clear cell renal carcinoma. Aptamers are short oligonucleotides that bind to their molecular target protein due to their 3D structure. Aptamers are mainly used for diagnostic and therapeutic applications. The cell-SELEX method is used to select the aptamers that specifically bind to a protein target on the cell surface.

In previous research cell-SELEX method has been used to select aptamers that bind to tumor cells, but the target protein to which they bind is unknown. Full research cycle in our study can be seen as the use of mass spectrometry for target protein identification. Most of the research publications still do not identify target proteins for aptamers that are selected using cell-SELEX thus significantly reducing the potential development of cell specific aptamer as a therapeutic or diagnostic agent. Target proteins need to be identified if it is planned to further develop aptamer for theranostic use. If the target protein is unknown, then it is not possible to describe how the aptamer binding to tumor cells affects different molecular signaling pathways within the cell.

Scientific novelty is can be seen as improved experimental design, including the use of patient matched normal and tumor primary cells. Use of high-throughput sequencing will allow to look at the aptamer selection process with increased sensitivity.


Andris Pavils Stikuts: Magnetic droplets; experiments and simulations

UL Faculty of Physics, Mathematics and Optometry. Advisor: prof. Andrejs Cebers

Magnetic droplets are made of single domain magnetic nanoparticles with a characteristic length of around 10 nm that are suspended in a liquid. Such droplets can be manipulated using an external magnetic field. The ability to know precisely how magnetic droplets behave under the applied field would allow for several interesting applications such as using these droplets in biological measurements on a microscopic scale.

During the thesis it is expected that a computer program will be created that for the first time will be able to calculate the dynamics of a magnetic droplet in a magnetic field in the general 3D case. This would enable the conduction of numerical experiments to quantitatively explore the behavior of the droplet under the influence of a magnetic field. This study would aid to understand the limits of existing theoretical models and help in understanding underlying physical processes in the dynamics of magnetic droplets.