ERC Advanced Grant AlgoRNN for Juergen Schmidhuber - Jobs for PostDocs & PhD Students
Deep Learning
Our impact on the world's most valuable public companies: Apple (#1), Alphabet (Google, #2), Microsoft (#3), Amazon (#5), ...
NIPS 2016 Symposium on RNNs
Recipient of the 2016 IEEE CIS Neural Networks Pioneer Award (announced in 2015) for pioneering contributions to deep learning and neural networks.
Very Deep Learning since 1991
Handwriting Recognition with Fast Deep Neural Nets & LSTM Recurrent Nets (Juergen Schmidhuber)
Switzerland - best country in the world? Leading the world in science, Nobel Prizes, patents, publications, citations, quality of life, competitiveness, happiness, many sports
The EU - a new kind of empire?
2011: First Superhuman Visual Pattern Recognition
The formal theory of creativity by Juergen Schmidhuber explains the desire to learn motor skills, to do science, to produce art
Learning Robots
Resilient machine with Continuous Self-Modeling
Statistical Robotics
Best robot car so far (Dickmanns, 1995)
Unsupervised Learning
Attentive vision
Unsupervised Neural Networks Fight in a Minimax Game
Juergen Schmidhuber at TU Munich Computer Science
STIFF - EU research project on enhancing biomorphic agility of robot arms and hands through variable stiffness & elasticity
The Swiss AI Lab IDSIA
What's new in Juergen Schmidhuber's page?
Kurt Goedel
Ray Solomonoff at IDSIA
Konrad Zuse
Archimedes, greatest scientist ever?
Master's Degree in Informatics with a Major in Intelligent Systems -  a master's in computer science, with a specialization in Artificial Intelligence
Home Page of Juergen Schmidhuber
AMA (ask me anything)
Juergen Schmidhuber and his iCub baby robot
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Jürgen Schmidhuber's ERC Advanced Grant AlgoRNN: Jobs for PostDocs & PhD Students

Update of 2020: we hired several excellent researchers, but still have open positions for PhD students and postdocs on the European Research Council (ERC) Advanced Grant on "Recurrent Neural Networks and Related Machines That Learn Algorithms" (AlgoRNN, compare our NIPS Symposium on this topic).

AlgoRNN blurb: Recurrent neural networks (RNNs) are general parallel-sequential computers. Some learn their programs or weights. Our LSTM was the first RNN to win machine learning contests, and is now used billions of times per day through the world's most valuable public companies, e.g., for speech recognition on over 2 billion smartphones, language translation, etc. Nevertheless, in lots of real-world tasks RNNs do not yet live up to their full potential. Although universal in theory, in practice they fail to learn certain important types of algorithms. This ERC project will go beyond today's best RNNs through novel RNN-like systems that address some of the biggest open RNN problems and hottest RNN research topics including attention & fast weights, RNN-based reinforcement learning, optimal transfer learning, and metalearning.

We have direct access to one of the fastest supercomputers in the world, with over 5000 GPUs, also based in Lugano, Switzerland.

We are looking for outstanding candidates with experience in topics such as deep learning neural networks, video analysis, RNNs and Long Short-Term Memory (LSTM), meta-learning or "learning to learn learning algorithms", evolutionary computation, RNN evolution, compressed network search, reinforcement learning (RL) for robots in realistic partially observable environments where traditional RL (for board games etc) does not work, unsupervised learning through adversarial neural networks that fight each other in a minimax game, curiosity-driven learning and the Formal Theory of Fun & Creativity, hierarchical reinforcement learning, general artificial intelligence, universal learning machines, search for programs running on general purpose computers such as RNNs, and "learning to think." (You will also collaborate with other lab members on other projects - we are one big family!)

The general goal is to advance the state of the art in machine learning and AI. Duration for postdocs: initially for 2 years, with possibility of prolongation.

We also have additional job openings with separate instructions thanks to generous gifts by Google DeepMind and NVIDIA (which also gave us a DGX-1 deep learning supercomputer), and an SNF grant on deep learning-based video analysis (we have filled positions for the RNNAIssance project).

We don't want to rush it, and will take our time to find excellent collaborators.

PhD student salary: Initially CHF 46'000 per year. Standard postdoc salary: CHF 80'000 per year. Low Swiss taxes. There is travel funding in case of papers accepted at important conferences. There are also exciting connections to the world of AI startups.

There is an opportunity to participate in teaching courses (in English) on machine learning, in Switzerland's first A.I. Master's degree program at the University of Lugano. Official language at IDSIA is English.

INSTRUCTIONS: 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 (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 erc, and 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 erc PhD. We will continually evaluate applications until all positions are filled, which may take a while - we will occasionally update this web site.

The Swiss AI Lab IDSIA was the smallest of the world's top ten AI labs listed in the 1997 "X-Lab Survey" by Business Week magazine. IDSIA's most important work was done after 1997 though. As of March 2017, the four most valuable public companies in the world (Apple, Alphabet/Google, Microsoft, Amazon) are heavily using IDSIA's deep learning algorithms. IDSIA won many international pattern recognition competitions, for example, the first official computer vision contests won by deep CNNs (2011). Its highly cited Ant Colony 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 Universal Artificial Intelligence and self-referential Universal Problem Solvers (previous work on general AI was dominated by heuristics). IDSIA's artificial Recurrent Neural Networks learn to solve numerous previous unlearnable sequence processing tasks through gradient descent, Artificial Evolution and other methods. In particular, IDSIA's LSTM RNNs are now on billions of smartphones and used billions of times a day, e.g., for speech recognition, or automatic translation through Google and Facebook. Also widely used: our adversarial neural networks that fight each other in a minimax game. Research topics also include complexity and generalization issues, unsupervised learning and information theory, forecasting, learning robots. 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. IDSIA is affiliated with the University of Lugano (USI) and USI's Faculty of Informatics and SUPSI. Many IDSIA alumni went on to become professors. Google DeepMind is heavily influenced by our former students: DeepMind's first PhDs in AI and Machine Learning came from our lab, one of them co-founder, one of them first employee; others joined later. IDSIA is located just outside the beautiful city of Lugano in Ticino (pics), the scenic southern Swiss province. Milano, Italy's center of fashion and finance, is 1 hour away, Venice 3 hours.

Switzerland is the world's leading science nation. It is the origin of special relativity (1905) and the 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, the most cited single-author paper, etc. In particular, according to The Times (2018), Switzerland leads the world in AI research in terms of citation impact (although China is the nation that produces the most papers on AI). As of 2017, Switzerland is the world's most competitive country for the 8th year in a row, according to the World Economic Forum. It's also the happiest country (1990s average), according to the Happiness Foundation. More.

The ERC (since 2005) is of the EU, which still boasts far more Nobel Prizes than any other political entity, and also (thanks in part to ERC) the largest chunk of recent high-impact publications, according to Nature (21 March 2017), which also states that ERC is "recognized as the best in the world in the way it supports fundamental research."

Unsupervised Neural Networks Fight in a Minimax Game
Deep Learning in Neural Networks: an Overview
Videos of talks by Juergen Schmidhuber
Robot Learning
History of computer vision contests won by deep CNNs on GPU
Artificial Curiosity
Feedback Network
Our impact on the world's most valuable public companies: Apple (#1), Alphabet (Google, #2), Microsoft (#3), Amazon (#5), ...
Reinforcement Learning
The RNN Book
My first Deep Learner of 1991 + Deep Learning timeline 1962-2013
Subgoal learning
Deep Learning neural nets won the MICCAI 2013 Grand Challenge on Mitosis Detection
Pybrain Machine Learning Library
Evolino for time series prediction
Genetic Programming
SSA learns a complex task involving two agents and two keys
Universal AI
Optimal Ordered Problem Solver
Goedel machine
Theory of Beauty and Low- complexity Art
Randomness and Kolmogorov complexity
Speed Prior
Computing the Universe
videos of talks on deep learning in the US
robot population explosion
Lugano, Switzerland
Juergen Schmidhuber's talks at DLD 2016 - Europe's hottest conference ticket
Brainstorm Open Source Software for Neural Networks
Juergen Schmidhuber at Singularity Summit 2009 - Compression Progress: The Algorithmic Principle Behind Curiosity and Creativity and Art and Science
Juergen Schmidhuber at the International Health Forum 2015. Credits: Wort & Bild Verlag / Eleana Hegerich
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ERC Advanced Grant AlgoRNN for Juergen Schmidhuber - Jobs for PostDocs & PhD Students