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Software Scientist: Data Analysis Services at th ... (No replies)

piero
3 years ago
piero 3 years ago
The Paul Scherrer Institute PSI is the largest research institute for natural and engineering sciences within Switzerland. We perform cutting-edge research in the fields of matter and materials, energy and environment, and human health. By performing fundamental and applied research, we work on sustainable solutions for major challenges facing society, science and economy. PSI is committed to the training of future generations. Therefore, about one quarter of our staff are post-docs, post-graduates or apprentices. Altogether, PSI employs 2100 people.

The Paul Scherrer Institute PSI is expanding its focus areas and establishing a new research division: Scientific Computing, Theory and Data. In recognition of the importance and globally unique ensemble of large facilities at PSI, a keystone to the new division is supporting the operations and experiments with their increasing challenges and opportunities for a unique digital environment. Within the division, the Science IT Infrastructure and Services department (Abteilung Wissenschaftliche IT-Infrastruktur und -Dienstleistungen, AWI) delivers scientific software, high performance computing and data lifecycle solutions to support research at the large scale research facilities and the wider PSI science program. Our in-house innovations and outreach activities with a world-leading national and international network of academic and industry collaborations is ensuring a growing platform to support the escalating opportunities from the digitalization of science.

Join our multidisciplinary SLS 2.0 software development team to ensure scientists can analyse and interpret petabytes of data in a distributed computing environment. We are looking for a Software Scientist / Data Analysis Services in the Science IT Infrastructure and Services/SLS2.0 unit.

Your tasks

You will join the Experiment IT Development and Operations Group in the new AWI department to play a central role in a collaboration with the Photon Science Division, aiming at improving and developing data analysis solutions targeting both on-premises and cloud-based High Performance Computing (HPC) clusters. As part of the recently approved SLS2.0 upgrade exciting efforts are underway at this internationally recognised facility to push forward the forefront of both scientific discoveries as well as data, computing research and development.

Your role will interface with the beamline teams and specialist cross-disciplinary groups providing functional, reliable, and efficient data analysis solutions to meet the external users’ demanding needs, both during and after an experiment.

Your main tasks will include:
  • Work closely with beamline scientists and users to improve existing workflows, as well as re-write and refactor current community algorithms and libraries for better performance on HPC clusters for the flagship SLS 2.0 beamlines
  • Set up a testing and monitoring environment for easy integration, benchmarking, and tuning of new algorithms
  • Identify and assess data analysis needs and requirements for SLS 2.0. Evaluate current best practice and performance tradeoffs on existing SLS beamlines and related scientific domains
  • Contribute to other ongoing efforts such as the overarching Controls and Science IT services and infrastructure for SLS 2.0
  • Collaborate with colleagues with similar functions at the PSI facilities along with leading national and international institutions and consortia

Your profile

As an enthusiastic and talented early career or experienced professional, you are motivated by interdisciplinary work at the interface between the experiment, data science, scientific software development and HPC. You are strongly interested in learning and building on others’ code, and will take pride in making algorithms run better and reliably on HPC. You have comprehensive knowledge of libraries and tools such as CUDA, MPI, OpenMP, GPU, job schedulers (e.g. SLURM) and message-queueing middleware for workflows. You have significant experience of programming in languages extensively used in science (C++/Python). Given the data volumes and methodology being used, you have experience of managing and organizing the parameters and results of large datasets.

The following expertise would be advantageous:

  • Prior experience in the analysis and reduction of large-scale experiment data, and/or image processing with/without machine learning and artificial intelligence
  • Experience on characterizing and improving the performance of scalable algorithms 
  • Experience on integrating and deploying workflows and software on HPC clusters

You hold a PhD degree (or equivalent practical experience) in computer science or in natural science with an emphasis in computer science, HPC, or data science. You are a good listener and a strong team player with excellent communication skills and sense of responsibility, fluent in English, speak German (an advantage not a must) or willing to learn it.

We offer

Our institution is based on an interdisciplinary, innovative and dynamic collaboration. You will profit from a systematic training on the job, in addition to personal development possibilities and our pronounced vocational training culture. If you wish to optimally combine work and family life or other personal interests, we are able to support you with our modern employment conditions and the on-site infrastructure. This is a fixed-term position.

The employment contract will be limited to the SLS 2.0, which ends 31 December 2025. The salary offer will be commensurate with the depth and breadth of your experience.

For further information, please contact Ms. Xingxing Yao, Email [email protected].

Please submit your application online by 5 September 2021 for the position as a Software Scientist / Data Analysis Services (index no. 7901-00).

Paul Scherrer Institut, Human Resources Management, Mariusz Prus, 5232 Villigen PSI, Switzerland

Apply online now



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Ab initio (from electronic structure) calculation of complex processes in materials