Molecular Biology (MSc/PhD)

Nikolay Grushetskiy

EDUCATION

College / University

Lomonosov Moscow State University

Highest Degree

Bachelor of Science

Major Subjects

Molecular Biology
Grushetskiy_1

Country

Russian Federation

Lab Experience

Molecular biology: molecular cloning (primer selection, DNA extraction and purification, bacterial transformation, plasmid assembly and amplification); PCR; agarose gel electrophoresis; CRISPR/Cas9 plasmid nucleofection; human cell lines (maintenance, cryopreservation, clone isolation); real-time PCR; flow cytometry (with assistance only); ImageJ
Software engineering: version control (git, gitlab, git lfs, clearml); Linux; C; Python (numpy, pandas, scikit-learn, pytorch, huggingface, yolo, langchain); machine learning (basic computer vision, natural language processing).

Projects / Research

  • 2023 – 2024: Bachelor Thesis “KMT2A-BTBD18 rearrangement associated with acute leukemia: modeling in cell culture”, Moscow State University, Molecular biology of oncogenesis group
  • 2023: “Species diversity and ecological characteristics of wood-decay fungi at the Zvenigorod Biological Station”, Moscow State University, Zvenigorod Biological Station
  • 2022: “Identification of common toad (Bufo bufo L.) individuals using deep convolutional neural networks”, Moscow State University, Zvenigorod Biological Station

Scholarships / Awards

2024 – 2025: Stipend by the International Max Planck Research School
2021 – 2024: Scholarship for Academic Excellence at Lomonosov Moscow State University
2018, 2019, 2020: Finals Award, All Russian School Olympiad in Biology

SCIENTIFIC INTERESTS AND GOALS

I was fascinated by the way complex biological systems are organized at the molecular level, and the way living matter is studied using its fundamental physical and chemical properties. My goal is to further my understanding of molecular biology and to continue looking at the world with curiosity. As a researcher, I have a specific interest in the application of Transformer-based models to protein sequences and structural data, as well as a broader interest in bioinformatics and machine learning, which I would like to deepen during the program. I believe that recent progress in Large Language Model research may open new opportunities in understanding protein sequence data.