PhD Dissertation LaTeX Template

\documentclass[12pt,a4paper]{book}
\usepackage[top=2.5cm,bottom=2.8cm,left=3cm,right=2.5cm,bindingoffset=0.5cm]{geometry}
\usepackage{amsmath,amsthm,amssymb}
\usepackage{xcolor}
\usepackage{tcolorbox}
\usepackage{titlesec}
\usepackage{fancyhdr}
\usepackage{setspace}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{tikz}
\usepackage{booktabs}
\usetikzlibrary{calc}

\tcbuselibrary{theorems,skins,breakable}

% ── Color palette ─────────────────────────────────────────────────────────────
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\onehalfspacing

% ── Chapter title style ───────────────────────────────────────────────────────
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% ── Theorem environments ──────────────────────────────────────────────────────
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\newtcbtheorem[use counter from=theorem]{corollary}{Corollary}
  {thmstyle=thmcol!70}{cor}

% ── Headers/footers ───────────────────────────────────────────────────────────
\pagestyle{fancy}
\fancyhf{}
\fancyhead[LE]{\small\textcolor{subtext}{\textit{Deep Learning for Scientific Document Understanding}}}
\fancyhead[RO]{\small\textcolor{subtext}{\leftmark}}
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\renewcommand{\headrulewidth}{0.3pt}
\renewcommand{\headrule}{\hbox to\headwidth{\color{rulecol}\leaders\hrule height\headrulewidth\hfill}}

\begin{document}

% ── Title page ────────────────────────────────────────────────────────────────
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\centering
\vspace*{1.2cm}
{\color{white}\Large\bfseries Deep Learning for Scientific\\[8pt]
Document Understanding}\par
\vspace{0.8cm}
{\color{rulecol}\rule{6cm}{0.6pt}}\par
\vspace{1cm}

\vspace{4cm}
{\large A.~J.~Martinez}\par
\vspace{0.4cm}
{\normalsize\textcolor{subtext}
  A dissertation submitted in partial fulfilment of the requirements\\
  for the degree of \textit{Doctor of Philosophy}}\par
\vspace{0.8cm}
{\normalsize\textcolor{subtext} Department of Computer Science\\
  University of Cambridge}\par
\vspace{0.4cm}
{\normalsize\textcolor{subtext} 2025}
\end{titlepage}

\frontmatter
\tableofcontents

\mainmatter

\chapter{Introduction}

The automatic understanding of scientific documents presents unique challenges at the intersection of natural language processing and mathematical reasoning. Modern transformer-based architectures have demonstrated remarkable ability to process structured text, yet the rich symbolic language of mathematics — with its equations, theorems, and logical dependencies — remains an open frontier.

This dissertation makes three primary contributions:
\begin{enumerate}
  \item A novel multimodal encoder for joint text and formula understanding.
  \item A large-scale benchmark of \num{100000} annotated scientific papers.
  \item State-of-the-art results on three downstream reasoning tasks.
\end{enumerate}

\chapter{Background}

\begin{definition}{Transformer Architecture}{transformer}
  A \emph{transformer} is a sequence-to-sequence model built entirely from
  self-attention and feed-forward layers, without recurrence.
  Given input embeddings $\mathbf{X}\in\mathbb{R}^{n\times d}$, the multi-head
  attention output is:
  \[
    \mathrm{Attention}(Q,K,V) = \mathrm{softmax}\!\left(\frac{QK^\top}{\sqrt{d_k}}\right)V.
  \]
\end{definition}

\begin{theorem}{Universal Approximation for Transformers}{universal}
  Transformers with sufficient depth and width can approximate any
  continuous sequence-to-sequence function on compact domains to
  arbitrary precision.
\end{theorem}

\end{document}