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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Motivation matters: How enrollment motives shape doctoral experiences and career aspirations

Plos One. 2025. Vol. 20. No. 9. Article e0330679.
Smirnov N., Tarasova E.

This study examines the motivational profiles of Russian doctoral students and their implications for academic success, satisfaction, and career aspirations. We employ data from a nationwide survey of doctoral students at Russian universities (N = 1,267). Using latent class analysis we identify four distinct motivational profiles: Academic Orientation, Unconscious Motives, Topic Devoted, and Everything Everywhere All at Once. Our findings reveal that students with academic orientations exhibit higher satisfaction, greater confidence in successfully defending their dissertations, and a stronger intention to pursue research careers. In contrast, students with unconscious motives demonstrate lower satisfaction, reduced confidence, and a weaker inclination toward research career paths. These findings highlight the role of unclear enrollment goals or non-academic motivations, such as military service deferment, in shaping doctoral outcomes. Additionally, our analysis shows that engagement in research is the only aspect of the doctoral experience that fosters research career aspirations among students with initially non-academic motivations. These findings emphasize the need for Russian universities to reconsider admission policies, doctoral program structures, and academic support mechanisms to better accommodate the increasingly diverse expectations of doctoral students. The study contributes to the broader discourse on doctoral education by providing empirical insights into the relationship between motivation, institutional factors, and career trajectories.

Research target: Education
Language: English
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Keywords: аспирантураlatent class analysisтеория самодетерминацииself-determination theoryлатентный классовый анализкарьерные траекторииcareer aspirationsdoctoral education in Russiaроссийская аспирантурамотивация аспирантов doctoral educationPhD motivation
Publication based on the results of:
Трансформация аспирантуры в Китае и России (2026)
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