Post-Doc Position and PhD Students

23 Feb 2024
Job Information

Organisation/Company

University of Trento

Research Field

Computer science » Computer architecture

Researcher Profile

Recognised Researcher (R2)
First Stage Researcher (R1)

Country

Italy

Application Deadline

20 Mar 2024 - 00:00 (UTC)

Type of Contract

To be defined

Job Status

Negotiable

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The High Performance Computing and Reliable System Laboratory at University of Trento invites applications from outstanding perspectives PhD Students and Post-Docs in the area of Parallel Computing and HPC.

The selected candidates will delve into key research areas, including but not limited to:

• Development of Parallel and Distributed-Memory Algorithms;
• GPU Computing and General Purpose Programming of AI accelerators;
• Advancements in Programming Models for Emerging Computing Systems;
• Research on HPC Interconnect Technologies and Their Applications;
• Supervision of Bachelor's and Master's Degree Students;
• Collaboration with Industry Partners and International Research Institutions

Specific research topics may include for example:

• design of efficient parallel and distributed-memory for sparse and irregular applications on RISC-V processors and GPUs;
• design scalable algorithms for static, dynamic or approximated graphs;
• Investigation of novel methodologies to accelerate and reduce the cost of Deep Learning;
• Investigation, benchmarking, and algorithms design for emerging systems (post Von Neumann/ post Moore) and programming models such as Processing In-memory.
• Investigation, benchmarking, and algorithms design for emerging HPC interconnect technology (e.g., in-network compute and SmartNIC);

Application Procedure:

Please submit your Expression of Interest by March 30 2024 by sending an email (see the contact below) by including your CV and up to 2 pages of your Research Statement.
Should your preliminary application be successful, you will be invited to provide a detailed formal application subsequently.

Essential Qualifications for Perspective PhD Students:

• Proficiency in C/C++ programming;
• Proficiency in High-Performance Computing (HPC) technologies and parallel programming frameworks, including CUDA, OpenMP, and MPI.
• Comprehensive understanding of computer system architectures.
• Solid foundation in fundamental algorithms and data structures for sparse matrices;

Essential Qualifications for Post-Docs:

• A PhD in computer science, information engineering, or a closely related field English proficiency certification (e.g., TOEFL or equivalent);
• Oustanding academic records. Ideal candidates must have a record of high-quality publications in Q1 journals (TDPS, FGCS ) or conferences (SC, PPoPP, IPDS, etc..) in the field of systems, parallel computing, HPC infrastructures, and machine learning.

Hicrest Laboratory combines expertise in parallel algorithms and high-performance computing with reliable computing systems by combining a hardware-software co-design approach. The synergy between our researchers, coming from multidisciplinary fields, is the key to the quest for producing the most performant, efficient, and reliable algorithms.
The lab offers an international and vibrant environment with a strong record of publications and has received best paper awards at venues like SC. We collaborate with top companies and universities around the world, aiming to contribute significantly to the field of HPC.

For further details, please send an email to:

Prof. Flavio Vella ( <name [dot]surname [at ] unitn [dot ] it >)

Requirements
Additional Information

Website for additional job details

https://www.hipeac.net/jobs/14550/post-doc-position-and-phd-students/

Work Location(s)

Number of offers available

2

Company/Institute

University of Trento

Country

Italy

City

Trento

Geofield

Where to apply

Website

https://hicrest.unitn.it/

Contact

City

Trento

Website

http://www.unitn.it/en

STATUS: EXPIRED