Connectionists: Fwd: Research positions in Neuromorphic computing in Singapore

[Yansong Chua]

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 following areas:

- 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 efficiency).

- 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 becomes critical.

- 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 neural networks.

- 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 international
scientists

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
).
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