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    <h2>Training Programs</h2>
    <p>We offer onsite training workshops ranging from introductory to state‑of‑the‑art topics. Each module can be tailored to your audience and delivered at your organisation.</p>
    <div class="list-section">
      <h3>Basics of Bayesian Inference</h3>
      <ul>
        <li>Probability theory fundamentals and Bayes’ rule</li>
        <li>Bayesian inference for inverse problems</li>
        <li>Case studies: signal deconvolution and computed tomography</li>
      </ul>
      <h3>Bayesian Computation Methods</h3>
      <ul>
        <li>Analytic computation for Gaussian models</li>
        <li>Laplace approximation and variational Bayesian approximation</li>
        <li>Message passing and approximate message passing algorithms</li>
      </ul>
      <h3>Data, Signal &amp; Image Processing</h3>
      <ul>
        <li>Data representations: plots, histograms and transforms (Fourier, time–frequency, wavelets)</li>
        <li>Case studies: interpolation, extrapolation and periodic component estimation</li>
        <li>General inverse problems and Bayesian inference tools</li>
      </ul>
      <h3>Inverse Problems in Medical Imaging</h3>
      <ul>
        <li>Overview of imaging systems (CT, MRI, PET, SPECT, etc.)</li>
        <li>Mathematical foundations: Fourier, Radon, Hilbert and Abel transforms</li>
        <li>Classical and model‑based iterative methods</li>
        <li>Bayesian inference and state‑of‑the‑art techniques</li>
      </ul>
      <h3>Big Data &amp; Deep Learning for Healthcare</h3>
      <ul>
        <li>Introduction to big data and its importance in bioinformatics and health informatics</li>
        <li>Three‑day workshop covering data pipelines, cloud computing and privacy issues</li>
      </ul>
      <h3>Bayesian Machine Learning &amp; Mixture Models</h3>
      <ul>
        <li>Parametric modelling and Bayesian parameter estimation</li>
        <li>Hierarchical models with hidden variables</li>
        <li>Maximum a posteriori, expected a posteriori and information criteria (AIC, BIC)</li>
        <li>Variational Bayesian approximation (VBA), expectation–maximisation (EM) and message passing</li>
        <li>Case studies: clustering and classification using Gaussian and Student‑t mixture models</li>
      </ul>
      <h3>Deep Learning</h3>
      <ul>
        <li>Introduction to deep learning, transfer learning and reinforcement learning</li>
        <li>Convolutional and recurrent neural networks for medical imaging</li>
        <li>Best practices for training and validating deep neural networks</li>
        <li>Big data management and privacy/security in medical applications</li>
      </ul>
      <h3>Introduction to Quantum Computing</h3>
      <ul>
        <li>Qubits, quantum gates, and superposition, Entanglement and measurement, Quantum circuits and the Dirac notation</li>
        <li>Quantum Programming Basics: Simulating quantum circuits, Visualizing quantum states and operations</li>
         </ul>
        <h3>Quantum Algorithms</h3> 
        <ul>
        <li>Deutsch-Jozsa algorithm, Grover’s algorithm,  search problems</li> 
        <li>Quantum phase estimation, Quantum Fourier Transform, HHL Algorithm for solving linear systems</li>
        <li>Applications of Quantum Algorithms: optimization, and cryptography</li> 
        <li>Variational and Hybrid Quantum Algorithms: Variational Quantum Eigensolver (VQE)</li>
        <li>Quantum Approximate Optimization Algorithm (QAOA)</li> 
        </ul>
        <h3>Quantum Machine Learning</h3> 
        <ul>
        <li>Quantum kernels and classification </li>
        <li>Quantum-enhanced feature spaces</li>
        <li>Case studies in QML</li>        
        </ul>
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