Top Numerical Scientist Award Archives

Mr. Maxence Berry | Extracellular nanoparticles | Best Researcher Award

Mr. Maxence Berry, University of Poitiers, France

Maxence Berry is a motivated and skilled graduate student specializing in physiological engineering, computer science, and biotechnology. With a strong foundation in bioinformatics and process engineering, he has gained valuable academic and professional experience through research internships, laboratory work, and collaborative projects. His interests lie in bioinformatics, bioprocess optimization, and the integration of computational tools to solve biological challenges.

PROFILE

Educational Detail

Master’s in Physiological Engineering, Computer Science, and Biotechnology
University of Poitiers (2023–2025)
Focus on project management, teamwork, and advanced biotechnological research.

Bachelor’s in Bioinformatics
University of Poitiers (2022–2023)
Specialization in computational tools for biological data analysis.

3rd Year of Engineering Cycle – Process and Bioprocess Engineering
Polytech Nantes, Saint-Nazaire (2021–2022)
Emphasis on bioprocess engineering and system modeling.

Preparatory Class for the Grandes Écoles – Technology Biology
Lycée Valentine Labbé La Madeleine (2018–2021)
Acquired rigorous work methods and analytical skills.

Technological High School Diploma
Lycée Françoise de Grâce Le Havre (2015–2018)
Gained foundational laboratory knowledge and practical skills in good laboratory practices.

Professional Experience

Research Intern – Laboratoire de Biophysique Clinique, Ljubljana, Slovenia
(May 2023 – July 2023)

Utilized interferometric optical microscopy to observe and quantify extracellular vesicles in blood plasma.

Authored an internship report and contributed to a publication (Elsevier).

Key Skills: English scientific writing, data analysis, knowledge transfer.

Research Intern – EBI KIT, Karlsruhe, Germany
(June 2022 – August 2022)

Investigated nitrogen and phosphorus removal in wastewater using partial denitrification processes.

Conducted nitrate concentration testing and reactor maintenance.

Key Skills: Reactor control software, experimental analysis, technical communication.

Laboratory Technician – SIDEL Blowing & Services, Octeville, France
(July 2019 – August 2019)

Performed calibration of measurement elements and explored business operations.

Key Skills: Teamwork, precision.

Research Interests

Bioinformatics and computational biology.

Process and bioprocess optimization.

Extracellular vesicle quantification and analysis using advanced imaging techniques.

Wastewater treatment and environmental sustainability.

Computer Skills

Programming Languages: Java, Python, Bash, Matlab, Ada.

Bioinformatics Tools: Cytoscape, Galaxy, GEO2R, OMICS, Microarray.

Web Development & Databases: PHP, CSS, HTML, MySQL.

Editing and Documentation: Microsoft Office Suite (Word, Excel, PowerPoint), LaTeX.

Top Notable Publications

Tang, M., Du, R., Cao, S., Berry, M., & Peng, Y. (2024). Tracing and utilizing nitrogen loss in wastewater treatment: The trade-off between performance improvement, energy saving, and carbon footprint reduction. Journal of Environmental Management, 349, 119525. [Cited 21 times].

Xu, D., Cao, S., Berry, M., Du, R., & Peng, Y. (2023). Granulation of partial denitrification sludge: Advances in mechanism understanding, technologies development, and perspectives. Science of the Total Environment, 904, 166760. [Cited 7 times].

Conclusion

Mr. Maxence Berry’s multidisciplinary expertise, demonstrated through high-quality academic training, impactful research projects, technical skills, and leadership roles, makes him a strong contender for the Research for Best Researcher Award. His notable achievements, such as publishing in Elsevier and conducting advanced research in extracellular vesicles and wastewater treatment, align with the award’s focus on research excellence.

Prof. Askhat Diveev, Federal research center Computer Science and Control of RAS, Russia

Prof. Askhat Diveev is a renowned computational scientist and expert in control systems and machine learning methods. Based at the Federal Research Center for Computer Science and Control of RAS, he has authored over 400 scientific publications and mentored multiple scholars in their academic journeys. His innovative contributions to numerical methods, genetic programming, and traffic flow modeling have earned him international recognition. His dedication to advancing computational sciences continues to inspire the global research community.

PROFILE

Orcid Profile

Educational Detail

Prof. Askhat Diveev has a distinguished academic foundation in computational science and numerical methods. He has mentored numerous scholars, successfully guiding eight candidates of sciences and one doctor of sciences.

Professional Experience

Prof. Diveev is a leading researcher at the Federal Research Center for Computer Science and Control, Russian Academy of Sciences (RAS). With over 400 published scientific papers and 148 indexed in Scopus, he holds a Hirsch index of 14 in Scopus and 9 in WoS. His expertise lies in the development of advanced numerical methods and machine learning techniques, which have been applied to various fields, including traffic flow modeling and control systems. Additionally, he has authored a book (ISBN: 978-3-030-83213-1) and holds significant contributions to applied mathematics and computer science.

Research Interests

Prof. Diveev’s research focuses on numerical methods for control problems, symbolic regression, genetic programming, and mathematical modeling. His groundbreaking work includes:

Developing a numerical method for general control synthesis using symbolic regression in machine learning.

Innovating the principle of small variations of the basis solution for mathematical expressions in non-numerical spaces.

Advancing variation genetic programming methodologies, including Cartesian and binary genetic programming.

Creating a recurrent, finite-difference mathematical model for urban traffic light control systems.

Top Notable Publications

1. Solving the Control Synthesis Problem Through Supervised Machine Learning of Symbolic Regression

Authors: A. Diveev et al.

Year: 2024 (November)

DOI: 10.3390/math12223595

Citations: Citation details currently unavailable; updates depend on indexing databases like Scopus or WoS.

2. Advanced Model with a Trajectory Tracking Stabilisation System and Feasible Solution of the Optimal Control Problem

Authors: A. Diveev et al.

Year: 2024 (October)

DOI: 10.3390/math12203193