Postdoc/PhD Opportunity at the University of California, Irvine

Postdoc/PhD Opportunity at the University of California, Irvine

[Ayoub Nouri]

[-- Attachment #1: Type: text/plain, Size: 108 bytes --]

[ The Types Forum (announcements only),
     http://lists.seas.upenn.edu/mailman/listinfo/types-announce ]


[-- Attachment #2.1: Type: text/plain, Size: 2857 bytes --]

[Apologies for cross-posting]

-------------------------------------------------------------------------------------------------

Postdoc/PhD Opportunity at the University of California, Irvine


Project Title: Conquering MPSoC Complexity with Principles of a 
Self-Aware Information Processing Factory

Host: Univ. of California, Irvine, Center for Embedded & Cyber-physical 
Systems, Irvine, CA, USA.

     Supervisor/Contact Person: Prof. Fadi Kurdahi (Kurdahi-sXc7qaQca9o@public.gmane.org) 
<mailto:Kurdahi-sXc7qaQca9o@public.gmane.org%29>

     Co-supervisor: Prof. Nikil Dutt (dutt-sXc7qaQca9o@public.gmane.org) 
<mailto:dutt-sXc7qaQca9o@public.gmane.org%29>.


Requirements:

     Postdoc: PhD degree in CS, Computer Engineering or EE from a top 
University.

     PhD: Master’s in Computer Science, Computer Engineering or 
equivalent from a top University


Objectives:

     Develop and evaluate hardware-assisted runtime verification models, 
architectures and tools.

     Develop evaluate machine learning-based specification mining 
methods and tools.


Expected Skills:

     Familiarity with hardware-assisted runtime verification formalism, 
tools and methods

     Familiarity with machine-learning algorithms

     Familiarity with specification mining methods and applications.

     Experience with FPGA tools

     Experience with multiprocessor simulators such as GEM5.


Planned visits and collaboration:

     TU Munich (Professor Andreas Herkersdorf)

     TU Branschweig (Professor Rolf Ernst)


Timeline:

    Ideally, candidates would be able to start in Winter or Spring 2018 
for a period of 1 year with possibility of extension up to 3 years total.


To Inquire: Please send a CV to Fadi Kurdahi (kurdahi-sXc7qaQca9o@public.gmane.org) 
<mailto:kurdahi-sXc7qaQca9o@public.gmane.org%29>.


Relevant publications:

Nikil Dutt, Fadi J. Kurdahi, Rolf Ernst, and Andreas Herkersdorf. 2016. 
Conquering MPSoC complexity with principles of a self-aware information 
processing factory. In /Proceedings of the Eleventh IEEE/ACM/IFIP 
International Conference on Hardware/Software Codesign and System 
Synthesis/ (CODES '16). ACM, New York, NY, USA, Article 37, 4 pages. 
DOI: https://doi.org/10.1145/2968456.2973275].

Ahmed Nassar, Fadi J. Kurdahi, and Wael Elsharkasy. 2015. NUVA: 
architectural support for runtime verification of parametric 
specifications over multicores. In /Proceedings of the 2015 
International Conference on Compilers, Architecture and Synthesis for 
Embedded Systems/ (CASES '15). IEEE Press, Piscataway, NJ, USA, 137-146.

A. Nassar, F. J. Kurdahi and S. R. Zantout, "Topaz: Mining high-level 
safety properties from logic simulation traces," /2016 Design, 
Automation & Test in Europe Conference & Exhibition (DATE)/, Dresden, 
2016, pp. 1473-1476

[-- Attachment #2.2: Type: text/html, Size: 6074 bytes --]

Postdoc Opportunity

[SMAIL NIAR]

[-- Attachment #1.1: Type: text/plain, Size: 3668 bytes --]

Post-Doc: Neural Networks & Heterogeneous Multi-Core Architectures for Autonomous Cars

Post-Doctorate position Progresses in the design of CMOS circuits have made the
possibility to support very complex Machine Learning (ML) algorithms using large
data sets. For this reason, AI techniques such as Convolution Neural Network
(CNN) and Deep Neural Network (DNN) have received recently interests both in
industry and academy to implement complex applications.

In the domain of embedded systems for automotive applications, CNN and DNN have
many potential applications, especially for Advanced Driving Assistance Systems
(ADAS) and autonomous driving. These algorithms have shown high performances in
scene understanding and object/obstacle classification.

This post-doc aims to contribute in the domain of embedded system design for
Real-time and automotive applications, especially in autonomous driving. The objective here is
to develop new heterogeneous FPGA/GPU-based multiprocessor architectures to
support complex ML algorithms. The target heterogeneous multi-core architectures
must in one hand adapt the ML algorithm and the supporting architecture to
different scenarios and on the other hand must use different CNN and DNN
configurations taking into account the different characteristics of embedded
sensors (Cameras, Lidars, Radars). 

The duties also include collaboration with PhD students working on these topics
and helping to write high-impact papers and funding applications.

The post-doc is within the framework of the ELSAT 2020 project
(http://www.frttm.fr/elsat2020 <http://www.frttm.fr/elsat2020>).

Bibliography:

1. Design of Multiple-Target Tracking System on Heterogeneous 
   System-on-Chip Devices, G. Zhong, S.Niar, A.Prakash, T.Mitra, IEEE 
   Trans. Vehicular Technology 65(6), 2016.
2. An Accelerator for High Efficient Vision Processing, Z. Du, 
   S.Liu, R.Fasthuber, T. Chen, P. Ienne, L. Li, T. Luo, Q. Guo, X. 
   Feng, Y. Chen, and O. Temam, IEEE Transactions on CAD of Integrated Circuits
   and Systems, 02/2017
3. Radar signature in multiple target tracking system for driver 
   assistant application, H. Liu, S. Niar, IEEE/ACM DATE 2013.
4. Computer Vision for Autonomous Vehicles, J.Janai, F. Güney, A.Behl, A.
   Geiger, Datasets and State-of-the-Art. CoRR, 2017.

Required degree and skills:

 *   Ph.D in computer engineering/electrical engineering/automation.
 *   Experience in scientific journals / conference publication with good English
   (writing and speaking).
 
   Knowledge/experience in one of the following matters would be an advantage:

1 Machine learning and AI techniques,

2 Signal and/or image processing,

3 Embedded FPGA-GPU-CPU systems, hardware architectures.

An application prepared in English or French should contain:

1.   CV with the list of publications.

2.   Contact information for 2 reference persons.

Salary: 2500 euros/month Deadline: 30/11/2017 Duration: 18 months 
Preferred starting date: 01/12/2017 but not later than 01/02/2018

Contact: Professor Smaïl NIAR Smail.niar-uXtqY6lGdSDBlfn6S3fvzW/KedPJEj8Q@public.gmane.org <mailto:Smail.niar-uXtqY6lGdSDBlfn6S3fvzW/KedPJEj8Q@public.gmane.org>
<mailto:Smail.niar-uXtqY6lGdSDBlfn6S3fvzW/KedPJEj8Q@public.gmane.org <mailto:Smail.niar-uXtqY6lGdSDBlfn6S3fvzW/KedPJEj8Q@public.gmane.org>> LAMIH/CNRS - University of
Valenciennes, France. www.univ-valenciennes.fr/LAMIH/membres/niar_smail <http://www.univ-valenciennes.fr/LAMIH/membres/niar_smail>
<http://www.univ-valenciennes.fr/LAMIH/membres/niar_smail <http://www.univ-valenciennes.fr/LAMIH/membres/niar_smail>>

[-- Attachment #1.2: Type: text/html, Size: 4946 bytes --]

[-- Attachment #2: ATT00001 --]
[-- Type: text/plain, Size: 157 bytes --]

_______________________________________________
Concurrency mailing list
Concurrency@listserver.tue.nl
http://listserver.tue.nl/mailman/listinfo/concurrency