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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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Exploring well-being of PhD students through latent profile analysis

Journal of Further and Higher Education. 2025. Vol. 49. No. 1. P. 115–135.
Gaete Sepulveda M. A., Volkova N., Kulbaeva A.

Well-being plays a crucial role in the completion of PhD studies. However, recent research suggests that the components of universities’ environment affect PhD students’ well-being differently, resulting in various outcomes. This research explores the well-being of PhD students, constructed as a series of latent profiles, and assesses their associations with the impact of COVID-19 restrictions and student satisfaction with a PhD programme in a research-intensive university. Drawn from an ecological system perspective, students with similar patterns of PhD well-being were identified through latent profile analysis. Among 208 Russian participants, we established four well-being profiles: ‘Disrupted well-being’ (20.2%), ‘Confident well-being’ (28.8%), ‘Dominated by Health and Research concerns’ (26.9%) and ‘Dominated by social connections concern’ (24.1%) groups. The ‘disrupted’ group reported poor well-being regarding five out of seven domains. These students are less satisfied with the PhD programme than other participants and demonstrate the strongest worries about career prospects and degree completion due to COVID-19. The ‘confident well-being’ group is the most sustainable and resilient, as these students report satisfaction with their studies, achieve excellent scores in well-being domains and have fewer concerns about the pandemic’s effect on their studies and degree completion. This profile mostly consists of male students. Satisfaction with the PhD programme contributes to the membership in this most desirable well-being profile. The two remaining groups demonstrated various alarming patterns of well-being, dominated by either ‘Health and Home’ and ‘Research’ domains or the ‘Social’ domain. This paper proposes recommendations for PhD programme managers and universities.

Research target: Education Economics and Management
Language: English
Full text
DOI
Keywords: satisfaction higher education Latent profile analysis doctoral educationPhD student well-being
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