Technical Skills

Numerical analysis and general mathematics: numerical methods, advanced scientific computing, finite element methods, iterative methods, ODE, PDE, analysis, linear algebra, math finance, 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.