Technical Skills

Numerical analysis and scientific computing: data assimilation, numerical methods for ODEs and PDEs, finite element and discontinuous Galerkin methods, computational fluid dynamics, turbulence modeling, Navier–Stokes equations, scientific machine learning, optimization, adaptive methods, structure-preserving methods, iterative methods, linear algebra, statistics, and applied stochastic methods.

Machine learning: linear regression, decision tree, KNN, logistic regression, neural networks, naive Bayes, PAC, MLE, MAP, K-Means, random forest, SVM, PCA, Boosting, Bagging.

Deep learning and reinforcement learning: CNN, RNN, LSTM, imitation learning, Monte Carlo methods, actor-critic, GAN, Q-Learning, policy gradient, model-based reinforcement learning, MDP.

Probabilistic and generative models: variational methods, inference methods, MCMC, Markov random fields, Bayesian networks, deep generative models (RBMs, VAEs, GANs).

Libraries and tools: Pytorch, TensorFlow, Keras, NumPy, SciPy, Pandas, Dolfinx, FEniCSx, FreeFEM, and SQL.

Skills: parallel computing on GPU and SMP clusters, time series analysis.

Coding languages: Python, JAVA, and MATLAB.