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First, I assume that by saying ‘after machine learning etc.’, you do not mean that ML, big data and algo trading will be gone. All of these will no doubt stay, though of course they will continue to evolve. I expect a wide propagation of methods based on Reinforcement Learning (RL) in most of areas of quantitative finance both within trading applications, and for other applications such as credit and market risk management. Conceptually, RL offers a kind of a paradigm shift, where the focus of a model is not on the predictive power and ability to predict the future, which is an auxiliary task, but rather on optimization of actions (trades, adjustments of hedges, loan approvals, etc.), which is a prime goal. ML, RL, and NLP will continue to evolve and be more widely applied to the management of both the B2B (commercial) and B2C (consumer) credit risk, such as customer acquisition and management of risk of credit cards, mortgages, auto- and student loans etc.On the other hand, I expect that RL itself will continue to evolve as well. Many RL methods currently available from robotics or video games are not well suited for financial applications, as they either are not robust when dealing with noisy data, or do not allow high enough dimensionally of the state-action space, or both. Most of practically interesting financial applications of RL need both robustness under noisy data, and an ability to work with a very high dimensional data. One promising approach to high-dimensional RL with noisy data is G-Learning which can be interpreted as entropy-regularized Q-learning. First financial applications of G-Learning for tasks of portfolio optimization and market modeling are suggested in this paper: Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy (Or, How We Learned to Stop Worrying and Love Bounded Rationality) by Igor Halperin, Ilya Feldshteyn :: SSRN.I expect that further progress will be due to emerging of approaches that combine flexibility of ML with more general principles of statistical learning theory a la Vapnik, as well as different physics-based methods that appeal to ideas of symmetries, analyticity and universalities of complex systems. One example of usefulness of this approach is given in this paper: 'Quantum Equilibrium-Disequilibrium': Asset Price Dynamics, Symmetry Breaking, and Defaults as Dissipative Instantons.

Open Web Application Security Project (OWASP) Summer Code SprintMozilla Security/Automation/Winter Of Security 2015Free and Open Source Software internships for people from underrepresented groupsRails Girls Summer of CodeTor Summer of PrivacyI'll add more as and when I remember some more.