[Comp-neuro] Research positions in Neuromorphic computing in Singapore
james4424 at gmail.com
Wed Dec 19 18:15:42 CET 2018
Multiple scientist and engineer positions are open for a neuromorphic
program (on-going till 2021) in Singapore, at the Institute for
Infocomm Research (I2R), A*STAR. The program is a multi-disciplinary
effort, that straddles across the hardware (neuromorphic chip with
RRAM, on-chip learning), middleware (emulator) and software (learning
algorithms), and we aim to develop a demonstrating application system
at end of the program. The program hence presents an unique research
opportunity for candidates hoping to build a complete neuromorphic
learning system. We are now in the second year of the program and have
made good progress on all three aspects of the program. We have still
a few openings for talented scientists and both software and hardware
engineers who would be excited to work on any aspects of the program
(hardware, middleware, software and system integration). Multiple
top-ranked universities and research institutes in Singapore (NUS,
NTU, IME, IHPC, I2R) are collaborating on the program.
The work package I am in-charge of is primarily involved in the design
of better algorithms for spiking neural networks. To this end,
successful candidates will conduct research in one or more of the
- Neuronal encoding: how to better encode external stimuli into spike
based representations to facilitate decoding with high fidelity and
also better learning performance (in terms of accuracy and power
- Supervised learning: given that spiking neural networks are
asynchronous and sparse in their activities, the design of supervised
learning algorithms that can fully capitalize on these properties
- Mapping of state-of-art deep learning networks to spiking networks.
Neuromorphic learning algorithms are still solving fairly simple
problems compared to deep learning. For this, we would like to
systematically borrow from the deep learning community networks and
learning algorithms that can quickly boost the capabilities of spiking
- Unsupervised learning. STDP is well suited for unsupervised learning
in spiking neural networks, and we would like to further advance STDP
learning in spiking neural networks (both theory and applications).
Preference will be given to candidates who can document knowledge in
deep learning, spiking neural networks or signal processing (with
interest in spiking neural networks).
Candidates must have a PhD (for scientists) or MS/BS (for engineers)
in computer science, computational neuroscience or related fields.
Strong programming and quantitative skills are highly desired.
Candidates should be proficient in spoken and written English.
The appointment will be for 3 years, and extended for another 1 year,
after review. Salaries are commensurate with
internationally-competitive salaries and benefits. The start date is
flexible and applications will be considered on a rolling basis until
the positions are filled.
Other benefits include:
- Funding for international conferences and training courses
- Collaboration opportunities with an excellent network of
Candidates please send your curriculum vitae, a statement of research
interests and three references to Dr. Yansong Chua
(chuays at i2r.a-star.edu.sg).
More information about the Comp-neuro