The position is now filled! We just hired Alex Graves (Cambridge).

Our budget is limited, and so we sadly were not able to consider several great candidates with impressive CVs. If you were among them then I do hope you are not too disappointed now, and I would like to thank you once more for your efforts, and I wish you all the best for your future carreer!
Juergen Schmidhuber

Note: we expect to have a new very similar job opening in the near future, with a focus on recurrent nets for robotics. In case you applied for the position above we'll keep your files. Otherwise please send a new application like the one requested in the old announcement below:

We are offering a fellowship for an outstanding PhD student interested in state-of-the-art artificial recurrent neural networks (RNNs). Possible backgrounds are computer science, physics, mathematics, etc. The initial appointment would be for 2 years, starting 2001 or 2002, with possibility of prolongation.

IDSIA is generally methodical and thorough in its professional searches, and may take several years to fill a position in a targeted field, so failure to make an appointment in any given year should not be misinterpreted as a loss of interest in that field.

The new student will interact with Juergen Schmidhuber and Doug Eck and other people at IDSIA. (Recently Felix Gers finished his PhD thesis on RNNs.)

Why RNNs? They can implement almost arbitrary sequential behavior. They are biologically more plausible and computationally more powerful than other adaptive models such as feedforward networks, Hidden Markov Models, Support Vector Machines, etc. Making RNNs learn from examples used to be difficult though. A recent novel RNN called "Long Short-Term Memory" (LSTM) overcomes problems of traditional RNNs, and efficiently learns previously unlearnable solutions to numerous tasks, using not more than O(1) computations per weight and time step: (1) Recognition of temporally extended, noisy patterns; (2) Recognition of regular and simple context free and context sensitive languages; (3) Recognition of temporal order of widely separated events; (4) Extraction of information conveyed by the temporal distance between events; (5) Generation of precisely timed rhythms, (6) Stable generation of smooth periodic trajectories; (7) Robust storage of high-precision real numbers across extended time intervals; (8) Prediction of chaotic and other time series, (9) Reinforcement learning in partially observable environments, (10) Metalearning of fast online learning algorithms.

Goal of the project is to further improve the state-of-the-art in RNN research, and to apply RNNs to interesting tasks including music composition and music interpretation. You might want to check out the following recent publications on RNNs:
S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735-1780, 1997.
F. A. Gers and J. Schmidhuber and F. Cummins. Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10):2451--2471, 2000.
F. A. Gers and J. Schmidhuber. LSTM recurrent networks learn simple context free and context sensitive languages. IEEE Transactions on Neural Networks, 2001, in press.

Please also find numerous additional publications on LSTM in the home pages of Juergen Schmidhuber, Doug Eck, and Felix Gers. Felix's home page also has pointers to LSTM source code.

SALARY: roughly SFR 35,000 per year. Low taxes. No teaching etc. - just research for PhD degree. There is travel funding in case of papers accepted at important conferences.

Applicants should submit : (i) Detailed curriculum vitae, (ii) List of three references and their email addresses, (iii) Concise statement of their research interests (two pages max). Please send all documents to:

Juergen Schmidhuber, IDSIA, Galleria 2, 6928 Manno (Lugano), Switzerland.

Applications in plain ASCII format can also be submitted by email (only small files please) to Do NOT send doc or pdf or large postscript files. Instead send WWW pointers to postscript files. Please connect your first and last name by a dot "." in the subject header, and add a meaningful extension. For instance, if your name is John Smith, then your messages could have headers such as:
subject: John.Smith.txt,
subject: John.Smith.statement.txt,
subject: John.Smith.correspondence.txt....
This will facilitate appropriate filing of your stuff. Thanks a lot!

ABOUT IDSIA. Our research focuses on artificial neural nets, reinforcement learning, complexity and generalization issues, unsupervised learning and information theory, forecasting, artificial ants, combinatorial optimization, evolutionary computation. IDSIA is small but visible, competitive, and influential. IDSIA's algorithms hold the world records for several important operations research benchmarks (see Nature 406(6791):39-42 for an overview of artificial ant algorithms developed at IDSIA). In the "X-Lab Survey" by Business Week magazine, IDSIA was ranked in fourth place in the category "COMPUTER SCIENCE - BIOLOGICALLY INSPIRED" - after the Santa Fe Institute, Stanford University, and EPFL (also in Switzerland). Its comparatively tiny size notwithstanding, IDSIA also ranked among the top ten labs worldwide in the broader category "ARTIFICIAL INTELLIGENCE".

IDSIA is located near the beautiful city of Lugano in Ticino (pictures), the scenic southernmost province of Switzerland, origin of special relativity and the WWW. Milano, Italy's center of fashion and finance, is 1 hour away, Venice 3 hours. Our collaborators at CSCS (the Swiss supercomputing center) are right beneath us; we are also affiliated with the University of Lugano and SUPSI. Switzerland boasts the highest citation impact factor, the highest supercomputing capacity pc (per capita), the most Nobel prizes pc (450 % of the US value), the highest income pc, and perhaps the best chocolate.

Juergen Schmidhuber, director, IDSIA, 2001
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