MacBook_Download/CL_NATECH.tex

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%   PERSONAL INFORMATION
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\name{Kevin}{Tongue}
\position{5th Year Student -- Mechanical Engineering \& Numerical Simulation}
\address{65, Rue Jean Parot, 42000 Saint-Étienne, France}

\mobile{(+33) 6 98 06 37 69}
\email{tonguekevin00@gmail.com}
\github{tittank1802}
\linkedin{tongue-kevin-52b100330}

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%   LETTER INFORMATION
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\recipient
  {PhD Selection Committee\\Laboratoire de Génie Chimique (LGC)}
  {INP Toulouse -- Université de Toulouse\\École Doctorale MEGeP\\Toulouse, France}
\date{\today}
\opening{Dear Members of the Selection Committee,}
\closing{Yours sincerely,}
\enclosure[Enclosed]{Curriculum Vitae, Academic Transcripts}

\begin{document}
\makecvheader
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\begin{cvletter}

\lettersection{Re: Application for PhD Position -- Ref.\ ABG-137018}

This PhD project addresses the critical challenge of quantifying NaTech risk---technological accidents triggered by natural hazards---by developing a predictive framework that couples \textbf{Extreme Value Theory (EVT)} with \textbf{Bayesian Networks (BN)}. The work further aims to model \textbf{pollutant transport driven by slope flows}, bridging deterministic fluid dynamics and stochastic risk modeling, and validating the approach against real field data from the CRBE observatory network. The planned international collaboration with Okayama University adds a valuable comparative dimension.

As a 5th-year Mechanical Engineering student at Centrale Lyon -- ENISE, completing a Master~2 in \textbf{Numerical Solid Mechanics}, I believe my combined expertise in \textbf{numerical simulation}, \textbf{stochastic--deterministic coupling}, and \textbf{scientific programming in Python} positions me well for this research.

\lettersection{Key Qualifications}

\textbf{(1) Deterministic--stochastic coupling and probabilistic modeling.} I am currently conducting my Master internship at IMT Mines Albi \& Saint-Étienne on the \textbf{modeling of granular segregation using inhomogeneous Markov chains}. The core methodology mirrors the spirit of this PhD: \textbf{deterministic simulations} (DEM) generate physical data that feed a \textbf{stochastic model} (time-dependent Markov chains) to predict macroscopic behavior under uncertainty. I also perform Monte Carlo sensitivity analyses on transition probabilities. This direct experience with coupling physical models to probabilistic frameworks is immediately transferable to the EVT--Bayesian Network architecture proposed in this thesis.

\textbf{(2) Numerical solver development and simulation expertise.} I possess strong skills in \textbf{FEM and CFD}, and have independently implemented a \textbf{fatigue failure prediction solver} based on the Dang Van multiaxial criterion, deployed at \texttt{huggingface.co/spaces/ktongue/simulations\_apps}. I also developed \textbf{Physics-Informed Neural Networks (PINNs)} for inverse identification of constitutive laws (\texttt{huggingface.co/spaces/ktongue/material\_identification}), demonstrating my capacity to integrate data-driven methods with physics-based constraints---directly relevant to calibrating Bayesian Networks from field observations and modeling pollutant transport on slopes.

\textbf{(3) Programming and quantitative risk analysis.} I am proficient in \textbf{Python} (NumPy, SciPy, PyTorch, Matplotlib), \textbf{C/C++}, Matlab, and Fortran, with proven experience in building, validating, and deploying numerical codes. These skills are essential for implementing EVT tail estimators, Bayesian inference algorithms, Monte Carlo uncertainty propagation, and CFD-based dispersion models central to this project.

\lettersection{Motivation}

The prospect of developing a decision-support tool that quantifies both structural and environmental impacts of NaTech events is both scientifically stimulating and socially meaningful. I am particularly motivated by the rare opportunity to validate models against real high-frequency field data and to engage in international collaboration at Okayama University. My \textbf{certified B2 English proficiency} ensures effective communication in this international research context.

Thank you for considering my application. I look forward to discussing how my profile can contribute to this project.

\end{cvletter}

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\end{document}