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September 25, 2026
AI Users Earn Up to 41.8% More Than Non-Users
Research conducted by economists at HSE University has revealed a significant correlation between the regular use of GenAI in the workplace and higher pay among Russian employees. The study found that individuals who frequently use GenAI in their professional activities earn notably more than those who reject these new tools or resort to them occasionally. The salary premium for highly qualified specialists reaches 41.8%. The article was published in the Voprosy Ekonomiki journal.
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‘Feedback and Constructive Criticism Are Essential in Our Profession
Vincent Fardeau, Associate Professor at HSE ICEF, has reached a major career milestone: he recently published his paper ‘Asymmetric Thin Markets’ in the Journal of Financial Economics, successfully passed his major academic review, and received tenure. In this interview, Vincent discusses the story behind the paper, explains the concept of asymmetric thin markets, and shares his advice for young scholars aiming to publish in top-tier journals.
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Personal Interest in Doctoral Thesis Topic Most Important for Confidence in Successful Defence
A researcher at HSE University analysed data on 1,539 doctoral students from 161 Russian universities to identify which features of a thesis topic are associated with academic success and engagement. The most important factor was found to be personal interest in the research topic, which was associated with almost all key aspects of doctoral programme experience—from engaging with the academic supervisor to research activity and confidence about successfully defending the thesis. The findings have been published in Higher Education.

 

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The Highly-Granular time-of-flight Neutron Detector for the BM@N experiment

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Нидерланды. 2025. No. 1072. Article 170152.
Morozov S., Bocharnikov V., Finogeev D., Golubeva M., Guber F.

A new Highly-Granular time-of-flight Neutron Detector (HGND) is being developed and constructed to measure azimuthal neutron flow and neutron yields in nucleus–nucleus interactions in heavy-ion collisions with energies up to 4A GeV in the fixed target experiment BM@N at JINR. Details of the detector design and results of performance studies for neutron identification and reconstruction are shown. Comparison of simulations for different options of the HGND layout at the BM@N is presented. Several proposed methods of neutron reconstruction including machine learning and cluster methods are discussed.

Research target: Physics
Language: English
DOI
Text on another site
Keywords: Neutron detectorsHeavy-ion collisionsSilicon photodetectorsTime-of-flight methods
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