See interview in FAZ (13 Dec 2021) and earlier blog post on KAUST
Hiring Faculty, PostDocs, PhD Students in AI at KAUST
I am directing the ambitious Artificial Intelligence Initiative at KAUST,
the university that boasts the world's highest impact per faculty—see my
recent blog post on this.
KAUST and its greater environment are now offering enormous resources to advance both fundamental and applied AI research.
For example, the new city
with an initial investment of USD 500 billion is expected to be full of AI use cases, and
the national SDAIA organization has huge amounts of data waiting to be mined through AI:
the entire region is prepared to be transformed through digitalization and AI.
We are now hiring outstanding professors, postdocs, PhD students, and others:
Sec. 1: Professors in Fundamental AI and Applied AI
Sec. 2: Teachers and Teaching Professors
Sec. 3: PostDocs and PhD Students for my own AI team
Sec. 4: AI Master Students
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 our size, KAUST will be presenting 17 full papers at the upcoming NeurIPS 2021 conference on machine learning).
1. Professors in Fundamental AI and Applied AI
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 (CBRC),
the Visual Computing Center (VCC),
the Extreme Computing Research Center (ECRC),
the Resilient Computing and Cybersecurity Center (RC3),
the KAUST Solar Center (KSC),
the KAUST Catalysis Center (KCC),
the Advanced Membranes and Porous Materials Center (AMPMC),
the Clean Combustion Research Center (CCRC),
the Petroleum Engineering Research Center (ANPERC),
the Water Desalination and Reuse Center,
the Center for Desert Agriculture,
and the Red Sea Research Center (RSRC).
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 (RISC) 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
For faculty applications, please use this link.
We'll develop rapid teaching programs for the basics of programming, deep learning, and other practically useful machine learning techniques for domain experts of the various KAUST centers mentioned in Sec. 1, such that they can quickly apply these techniques to data collected in their own domain. For example, hands-on courses of our Deep Learning Lab will help experts in chemistry to train artificial neural networks to become artificial chemists that learn to predict the outcome of chemical reactions, given chemical substances and conditions such as pressure, temperature, catalysts, etc. Such artificial chemists can be used to identify new promising experiments, greatly reducing the search space for drug design etc. Similar for biology, material science, solar cells, etc.
2. Teachers and Teaching Professors
The courses developed in this program should also evolve into online courses, expanding the reach to a broader national and international audience.
For this we are seeking more Teaching Professors, Teachers, and Teaching Assistants on all levels. Please apply by
emailing us at email@example.com.
My own research group 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
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.
See our recent papers at arXiv and the AI Blog to understand better what we are interested in.
You will also
collaborate with other lab members (and with my separate team in Switzerland) on other projects.
3. PostDocs and PhD Students for my own AI team
The general goal is to advance the state of the art in machine learning and AI.
The duration for postdocs is initially 2 years, with possibility of prolongation.
KAUST offers competitive salaries. Accommodation is provided cost-free. No taxes.
There is travel funding in case of papers accepted at important conferences.
There are also exciting
to the world of AI startups.
The KAUST campus is very family-friendly, offering all types of relevant schools for kids and others.
Instructions for PostDocs and PhD Students: submit your CV, a brief statement of
research interests explaining why exactly you'd like to work with us,
and a list of 3 references and their
email addresses to firstname.lastname@example.org. Keep it short
(do not send articles or transcripts or other large files—they will be deleted).
In the subject header,
mention your full name followed by the keyword KAUST, add "PhD" in case of PhD students, or add "postdoc" in case of postdocs.
For example, if your name is Jo Mo, and you are applying as a PhD student,
use subject: Jo Mo KAUST PhD.
We will continually evaluate applications, which may take a while—we will occasionally update this web site.
PhD student candidates should also upload their
material under this link.
For master student admissions, please use this link.
4. AI Master Students