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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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Choosing to succeed? Insights into doctoral students’ supervisor selection and its outcomes

Plos One. 2025. Vol. 20. No. 7. Article e0328471.
Pavliuk D., Zhuchkova S.

Doctoral student experience and outcomes largely depend on the quality of supervision. Research on doctoral supervision usually focuses on the attributes, styles, and roles of a supervisor during doctoral training, while the process and effects of initiating supervisor-supervisee relationships remain less explored. This paper aims to examine how students’ strategies for supervisor selection are related to subsequent difficulties in interaction with their supervisors and their confidence about their future dissertation defense. Based on a nationwide survey of doctoral students in Russia (N = 1796) and regression analysis, we demonstrate that academic criteria such as a supervisor’s subject-matter expertise and successful supervision history are the best factors for students to consider to reduce the probability of future difficulties with the supervisor and to avoid experiencing a lack of confidence. Using an existing positive personal relationship with a supervisor as a criterion reduces the chances of interaction difficulties, but does not affect a student’s confidence in future academic success. At the same time, the “only option” scenario (i.e., a situation when students have no alternatives to choose from) is associated with higher chances of facing difficulties during the doctoral journey and a lower level of student confidence. The study highlights the critical role of agency in supervisor selection, showing that structured support for informed decision-making – whether through institutional matchmaking systems or student mentoring programs – can mitigate risks of poor supervision outcomes. These findings call for policy interventions to promote transparency in supervisor selection processes, especially in contexts where students have limited choices. The results of the research can be used by prospective doctoral students and doctoral program administrators to create a personal or institutional navigation system for choosing a supervisor.

Research target: Education
Language: English
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Keywords: аспирантураdoctoral supervisionнаучное руководство аспирантамиdoctoral students doctoral education
Publication based on the results of:
Undergraduate and doctoral student experience in the context of the student body diversification (2024)
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