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Position: PhD Position in High Performance Computing
Institution: Universität Basel
Location: Basel, Switzerland
Duties: The topic of this Ph.D. will be on developing and applying hierarchical scheduling solutions for pipelines and workflows originating in mixed DM, HPC, and ML workloads, with emphasis on hierarchical topology-aware task and data placement, non-uniform memory access awareness, and performance optimization in multi-tenant resource sharing scenarios
Requirements: A Master's degree (or equivalent) in Computer Science, Computer Engineering, or Mathematics; Very good programming skills in C, C++, Java, or Python; Good understanding of parallel programming; Good knowledge of Linux; Basic background in distributed systems, compilers, data management, machine learning; Fluency in English (verbally and in writing), while knowledge of German, although not required, can be a plus; High motivation, strong team-working abilities, solid analytical, problem-solving, and communication skills; Experience in carrying out research projects and writing scientific articles will be considered a plus; Experience with any of the following frameworks is considered as a plus: Slurm, Apache Spark, YARN, Mesos, or Kubernetes
   
Text: PhD Position in High Performance Computing 100% The High Performance Computing (HPC) research group (lead by Prof. Florina M. Ciorba) is seeking a highly talented and motivated PhD student to conduct high quality research, publish in top venues, and pursue a doctoral degree in Computer Science , with a focus on HPC. The position is fully funded (100%) for 4 years in the context of the EU project "DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning". The DAPHNE project gathers the expertise of 13 distinguished academic and industrial partners from 7 European countries. The goal is to define and build an open and extensible system infrastructure for integrated data analysis pipelines, including data management (DM) and processing, HPC , and machine learning (ML) training and scoring. Although the hardware stacks of clusters and provisioned clouds for DM, HPC, and ML converge rapidly, programming paradigms, cluster resource management, and data formats differ substantially across DM, HPC, and ML software stacks. However, there is a trend toward complex data analysis pipelines that combine these different stacks. In the DAPHNE project, the consortium will systematically investigate the necessary system infrastructure, language abstractions, compilation, and runtime techniques, systems, and tools needed to increase productivity when building such data analysis pipelines, eliminating unnecessary performance bottlenecks. Your position The topic of this Ph.D. will be on developing and applying hierarchical scheduling solutions for pipelines and workflows originating in mixed DM, HPC, and ML workloads, with emphasis on hierarchical topology-aware task and data placement, non-uniform memory access awareness, and performance optimization in multi-tenant resource sharing scenarios. Your profile A Master's degree (or equivalent) in Computer Science, Computer Engineering, or Mathematics. Very good programming skills in C, C++, Java, or Python. Good understanding of parallel programming. Good knowledge of Linux. Basic background in distributed systems, compilers, data management, machine learning. Fluency in English (verbally and in writing), while knowledge of German, although not required, can be a plus. High motivation, strong team-working abilities, solid analytical, problem-solving, and communication skills. Experience in carrying out research projects and writing scientific articles will be considered a plus. Experience with any of the following frameworks is considered as a plus: Slurm, Apache Spark, YARN, Mesos, or Kubernetes. We offer you A doctoral dissertation topic with significant impact on the scientific community. Close supervision on your Ph.D. research, working with and access to international collaborators, powerful supercomputers, and networking opportunities. High impact research in system software for performance optimization using HPC. A dynamic and supportive working environment and a good working atmosphere. Application / Contact Inquiries about this position can be sent to the group leader Prof. Dr. Florina M. Ciorba ( florina.ciorba@unibas.ch ). Please send your application (see guidelines below) until October 15, 202 0 to the above email address, and specify " Ph.D. Position DAPHNE " in the subject line. Include the following documents in your application as a single PDF file: A brief (200 words) personal statement explaining your interest and motivation for a PhD in Basel in the HPC group Recent curriculum vitae Links to publications and/or recent academic work Links to/attachments of examples of personal contributions to software (GitHub, Bitbucket, etc.) Contact info (no direct recommendation letters) for peers that can recommend you (upon request) www.unibas.ch
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