We are seeking
researchers with experience / interest in topics such as
reinforcement learning (RL),
feedback networks (RNN),
Goal: to transform state of the art RL
algorithms (policy gradients, evolutionary methods etc)
into biologically plausible local learning
rules for spiking neurons, and to apply them to artificial agents
such as learning robots, in
collaboration with our partners at EPFL and Berne.
Salary: commensurate with experience; Postdoc ~ SFR 72,000 / year (~ US$ 69,700 / year as of 7 Jan 2010);
PhD student ~ SFR 38,000 / year (~ US$ 36,700). Low taxes.
Submit your CV, a list of 3 references and their
email addresses, and a statement of relevant research interests, to
In the subject header,
mention name and job type and keyword sin2008.
For example, if your name is Jo Mo and you want the PhD fellowship, use
subject: Jo Mo PhD sin2008
Interviews. Most interviews will
take place at IDSIA, but we will also arrange Skype video interviews.
We already conducted several
at the Singularity Summit in NYC (3-4 Oct 2009)
and at the EUCogII meeting in Hamburg (10-11 Oct 2009).
And we'll do more interviews at CogSys 2010 (Zurich, Jan 27-28), EUCogII 2010 (Zurich, Jan 29), and
at AGI 2010 in Lugano.
Switzerland is a good place for scientists.
It is the origin of special relativity (1905)
World Wide Web (1990),
is associated with 105 Nobel laureates, and
boasts far more Nobel prizes per capita than any other nation.
It also has the world's highest number of publications per capita,
the highest number of patents per capita,
the highest citation impact factor,
most cited single-author paper,
the biggest and most expensive machine ever,
Switzerland also got the
highest ranking in the
list of happiest countries.
is small but visible, competitive, and influential.
For example, its
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, Reinforcement Learning, 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.