-
machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work independently, has a passion for AI
-
regulatory networks from single-cell genomics data, including RNA-seq and ATAC-seq, and to predict phenotypic outcomes of genetic perturbations. Our work primarily uses the model plant Arabidopsis. Funded
-
mechanical engineering, or related field to apply. A strong publication record is encouraged and previous experience in areas such as Neural network vulnerabilities and defenses, Anomaly detection, Adversarial
-
via nonlinear parametrizations such as deep networks, dynamical systems and control, Bayesian inference and generative modeling, and randomized linear algebra. Applications of interest are transport
-
. Preferred Experience designing studies in vehicle/driving simulators (fixed-base or VR-based). Unity3D development (C#), including multiplayer and networking (e.g., Netcode for GameObjects, Mirror, or Photon
-
concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical