2013/2014: Join the team that
won more competitions in machine learning and pattern recognition than any other team:
JOBS for Postdocs (& PhD Students) in Machine Learning & Evolution & Robotics
in Jürgen Schmidhuber's group at the
Swiss AI Lab IDSIA,
especially for the
initially for 2 years, with possibility of prolongation.
Update of 5 Nov 2013: we have received many applications, and started hiring,
but we still have not found the perfect ProtoTouch postdoc (to be funded by
a prestigious Marie Curie Experienced Researcher Fellowship) -
its online form reopened on 5 Nov - next deadline 8 Dec - see column to the right.
We are seeking outstanding postdocs (and PhD students)
with experience / interest in topics such as
deep learning neural networks (NN),
recurrent neural networks (RNN),
curiosity-driven learning & intrinsic motivations based on our theory of surprise and interestingness and the Formal Theory of Fun & Creativity,
computer vision and 3D animation,
reinforcement learning & policy gradients
for partially observable environments,
hierarchical reinforcement learning,
statistical / Bayesian approaches to machine learning,
universal learning machines.
The general goal is to advance the state of the art in machine learning and AI,
in the context of various concrete research projects.
Most of the funding is provided by four research projects outlined below.
For now, we are mainly interested in candidates applying for a prestigious Marie Curie Experienced Researcher Fellowship
for the ProtoTouch project.
But you will also
collaborate on other projects with other lab members -
we are one big family!
1. The ProtoTouch
(EU-PEOPLE) project investigates novel touch-based user interfaces (e.g., touch screens for mobile devices, touch pads for computers). In a consortium of 10 European research institutes, you will help to develop and apply machine learning methods such as deep learning neural networks to investigate the performance of novel tactile displays, and biological processes responsible for touch. Candidates should have multidisciplinary research interests in areas like machine learning, electronic devices and neurophysiology.
ProtoTouch candidates must not have resided or carried out their main activity in Switzerland for more than 1 year in the 3 years immediately prior to their recruitment.
Postdocs must fulfil the "experienced researcher" requirements of the Marie Curie regulations: at least 4 years but less than 5 years of "full time research experience." PhD students must fulfil the "early-stage researcher" requirements.
See page 4 of the Marie Curie guidelines (PDF).
2. A general SNF research project aims at improving methods for deep learning and
3. The NASCENCE
EU (STREP) project requires expertise in machine learning
and evolutionary computation (genetic algorithms, evolution
strategies, estimation algorithms, neuroevolution).
We intend to apply
evolutionary algorithms to automatically discover the
electrical signals which transform a nano-particle substrate
(e.g. networks of nanoparticles, carbon nanotubes or films of
graphene) into useful computational circuits.
4. The WAY
EU (STREP) project on wearable interfaces and hand function recovery requires signal processing for EEG analysis combined with machine learning (recurrent neural networks, optimization). The goal is to develop and apply algorithms that facilitate control of hand prostheses and hand exoskeletons through brain-computer interfaces, in collaboration with partner institutes. We focus on practical application of hand-assistive devices, with a strong involvement of patients in clinical trials.
Our international project partners include neuroscientists,
mathematicians, psychologists, roboticists, and other
experts from the UK, Germany, Italy, Scandinavia, France, and the US.
Start: 2013 (or early 2014).
The Swiss AI Lab IDSIA
was the smallest of the world's top ten AI labs listed in the
"X-Lab Survey" by Business Week magazine,
and ranked in fourth place in the category "Computer Science -
Biologically Inspired". IDSIA's most important
work was done after 1997 though.
It is small but visible, competitive, and influential.
For example, it
recently won many international pattern recognition competitions.
Its highly cited
Optimization Algorithms broke numerous benchmark records and
are now widely used in industry for routing, logistics etc (today
entire conferences specialize on Artificial Ants).
IDSIA is also the origin of the first mathematical theory of optimal
Artificial Intelligence and self-referential
Universal Problem Solvers (previous work on general
AI was dominated by heuristics).
Recurrent Neural Networks
learn to solve numerous previous unlearnable sequence processing
tasks through gradient descent,
Evolution and other methods.
Research topics also include
complexity and generalization issues,
unsupervised learning and information theory,
IDSIA's results were reviewed not only in
science journals such as Nature, Science, Scientific American,
but also in numerous popular press articles in
TIME magazine, the New York Times,
der SPIEGEL, and many others. Numerous TV shows on Tech & Science
helped to popularize IDSIA's achievements.