Dimitrios Papanikolaou
Tutor: Agatino MUSUMARRA
"Dimitrios Papanikolaou - Curriculum vitae
PERSONAL
INFORMATION
Name
e-mail address
Phone number
Date of birth
Nationality
Dimitrios Papanikolaou
dimitrpapanik@gmail.com
+30 6980157284
20/01/1999
Greek
ACADEMIC
INTERESTS
Experimental Nuclear Physics
Nuclear Astrophysics
Neutron Physics
Neutron-induced reactions
Detector Physics
EDUCATION
-Oct. 2024 – To date
Ph.D. course in Complex systems for physical, socio-
economic and life science
University of Catania (DFA), Catania (Italy)
-Oct. 2022 – July 2024
Master in Experimental Physics
University of Ioannina, Ioannina (Greece)
Thesis Title: “Neutron capture reactions for nuclear astrophysics:
Development & characterization of an innovative detection setup
based on trans-Stilbene organic scintillators”
Grade: 9.08 / 10.0
-Jan. 2024
n_TOF Nuclear Physics Winter School 2024 St. Gervais les
Bains, France
-Oct. 2023 – Dec. 2023
Erasmus+ Traineeship University of Catania (DFA),
Project: Assembly and characterization of solid-state Stilebene
detectors for n-gamma capture measurements
-2017 - 2022
Bachelor in Physics
University of Ioannina, Ioannina (Greece)
Thesis Title: “Study of HPGe detector shielding for use in
inelastic neutron scattering experiments at the n_TOF/CERN
facility”
DIGITAL SKILLS
-Operating Systems
* Windows
* Linux/Unix
-Programming languages
* Good knowledge of Python, C/C++
* Basic knowledge of IDL, Octave, HTML/CSS
-Scientific Software
* ROOT (Data analysis)
* GEANT4 (Particle transport code)
* LabVIEW (Graphical programming environment)
* Fiji - ImageJ (Image processing package)
-Other software
* Microsoft Office Suite (Word, PowerPoint, Excel)
* LibreOffice (Writer, Impress, Calc)
ACADEMIC EXPERIENCE
-Lab-work
* Assistant in laboratory courses in modern physics
University of Ioannina, Ioannina (Greece)
-Teaching experience
* Tutoring support and analysis consultation for
undergraduate diploma thesis submission
OTHER SKILLS / LANGUAGES
-Soft skills
* Attention to detail | Communication
Quick Learner | Adaptable | Punctual
Problem Solving | Organized
-Mother tongue
* Greek
-Foreign languages
* English - Certificate of Proficiency in English, University of
Michigan (C2)
* German - Goethe Institute (B1)
CONFERENCES / SEMINARS
-27 - 28 Sept. 2024
Oral Presentation - D. Papanikolaou, et al., “Neutron capture
reactions for nuclear astrophysics: Development &
characterization of an innovative detection setup based on trans-
Stilbene organic scintillators” - HNPS2024, ATH
-22 - 27 Sept. 2024
Oral Presentation - D. Papanikolaou, et al., “SOLARIS” a
neutron tracker for the next generation solar missions -
ANP2024, ATH
-31 May - 1 June 2024
7th International Workshop of the Hellenic Institute of Nuclear
Physics (HINPw7), UOI
-7 - 8 Oct. 2022
Poster Presentation - D. Papanikolaou et al., “Study of HPGe
detector shielding for use in inelastic neutron scattering
experiments at the n_TOF/CERN facility”, HNPS2022
-15 - 18 May 2018
17th Panhellenic Conference of the Association of Greek
Physicists - Physics Meets Society, Thessaloniki
PUBLICATIONS IN CONFERENCE PROCEEDINGS
- D. Papanikolaou et al., HNPS 2022, “Study of HPGe detector shielding for use in inelastic neutron
scattering experiments at the n_TOF/CERN facility”. 2022
- A. Musumarra, D. Papanikolaou, HINPw7 2024, “Towards the next generation of detectors for n-γ
capture reactions at n_TOF”. 2024
Last updated: October 2024"
Tailoring a neutron multidetector array: A machine learning approach to optimization & characterization.
The development of advanced neutron detectors is critical for a wide range of applications in Nuclear Physics and related fields such as Astrophysics (Space Environment and Space Weather), Neutron Dosimetry and Homeland Security. Traditional approaches to detector design follow a deterministic path involving conceptualization, design, simulation, and characterization. However, recent advances in Complex Systems and Machine Learning (ML) have revolutionized this field, enabling more efficient and cost-effective optimization of detector configurations. This research proposal outlines a project aimed at leveraging machine learning to enhance the performance of a new neutron multidetector array, specifically designed for space missions and neutron flux monitoring.