Hiring Faculty, PostDocs, and PhD Students in AI at KAUST (university with highest impact per faculty)

[Schmidhuber Juergen]

I am directing the ambitious Artificial Intelligence Initiative at KAUST, the university that boasts the world's highest impact per faculty. KAUST and its greater environment are now offering enormous resources to advance both fundamental and applied AI research. We are hiring outstanding professors, postdocs, and PhD students in AI and related fields.

Some of the new faculty will focus on Fundamental AI research in areas such as artificial neural networks and deep learning, reinforcement learning and planning, artificial evolution, probabilistic reasoning, natural language processing, computer vision (already very visible at KAUST), automation, robotics, AI theory based on algorithmic information theory, unsupervised/self-supervised learning, metalearning, zero-shot learning, and other fields. The broad goal is to advance fundamental AI research on all fronts.

The new professors will join a growing team that's already highly visible (in direct contrast to its size, KAUST will be presenting 17 full papers at the upcoming NeurIPS 2021 conference on machine learning).

Other faculty will focus on Applied AI in areas such as healthcare (in conjunction with KAUST's Smart Health Initiative), drug design, chemistry, molecular biology, climate, earth and environmental science (in conjunction with KAUST's Climate and Livability Initiative), material science, solar cells, and robotics, extending present strengths of KAUST (see the KAUST Discovery Magazines). Here we are seeking researchers whose main expertise lies in a field related to the ones above, but with a track record of collecting and managing relevant data for machine learning applications, e.g., through automated experiment design for data collection in biology/chemistry/material science/other areas, and who wish to scale this up in one of the existing KAUST research centers, with the goal of revolutionizing their field: the Computational Bioscience Research Center, the Visual Computing Center, the Extreme Computing Research Center, the Resilient Computing and Cybersecurity Center, the KAUST Solar Center, the KAUST Catalysis Center, the Advanced Membranes and Porous Materials Center, the Clean Combustion Research Center, the Petroleum Engineering Research Center, the Water Desalination and Reuse Center, the Center for Desert Agriculture, and the Red Sea Research Center. Some of the Centers above have additional job openings - please check them out!

We are also searching for experts interested in combining reinforcement learning and soft robotics, with the support of KAUST's Robotics, Intelligent Systems, and Control Lab, to build novel robust physical machines with lots of sensors and actuators, learning and collecting lots of data like babies through self-invented experiments, but without being able to damage themselves.

However, we are also interested in outstanding researchers who don't fit any of the above categories.

I'll be happy to answer questions related to these positions—please don't hesitate to contact me via email under
ai-facu...@kaust.edu.sa

We are also hiring teaching professors, teachers, and teaching assistants on all levels.

I am also hiring postdocs and PhD students for my own research group, which will continue to focus on the deepest and most popular artificial neural networks (NNs) including the LSTM family, the closely related Highway Net family, variants of attention-based Transformers and other Fast Weight Programmers, fast and deep convolutional NNs for image recognition, unsupervised and self-supervised learning, and combinations thereof. We are especially interested in reinforcement learning (RL) and planning for robots in realistic partially observable environments - where traditional RL (for board games etc) does not work -, policy gradients, evolutionary computation, compressed network search, Artificial Curiosity and GANs, the formal theory of fun & creativity, hierarchical reinforcement learning, automatic discovery of (hierarchically) structured concept spaces based on abstract objects, metalearning machines that learn to learn, universal learning machines, optimal search for programs running on general purpose computers such as recurrent neural networks, "learning to think," and Soft Robotics. You may also collaborate with other lab members (and with my separate team in Switzerland) on other projects.

Family-friendly KAUST offers competitive salaries. Accommodation is provided cost-free. No taxes.

Please follow instructions under

https://cemse.kaust.edu.sa/ai/hiring-faculty-postdocs-phd-students-ai

Jürgen


Jürgen Schmidhuber
Director, AI Initiative, KAUST
juergen.s...@kaust.edu.sa