The Cluster of Excellence "Machine Learning - New Perspectives for Science" at the University of Tübingen offers a position as
HPC/AI Cluster Administrator (m/w/d, E13 TV-L, 100%)
The position runs until 31st December 2025.
High performance computing (HPC) is essential for competitiveness of science and industry. Modern AI-focused research is unthinkable without such infrastructure. As such, you will play a pivotal role in developing and administering infrastructure and tools for scalable AI-focused scientific computing on heterogeneous systems. As part of a motivated team, you will design, build, and maintain the cluster. You will realize several large-scale future upgrades with the latest AI accelerators, networking and storage technologies. In addition, you will work and communicate with the users, help to efficiently scale our largest AI experiments and enable an ambitious research agenda through a stable, accessible and performant cluster.
What You'll Do
• Administration of complex HPC architectures with specialization(s) in sub-areas such as parallel file systems, management of heterogeneous resources with Slurm, system deployment and automated system monitoring.
• Recommend and develop modifications and enhancements to existing hardware and software, new implementations, and installation standards to increase system utilization, stability for end-users and improve monitoring.
• Work and communicate with the users, help with scalability of AI experiments and enable an ambitious research agenda through a stable, accessible and performant cluster.
• Facilitate the procurement of HPC components and related, including tender writing and bid evaluation.
What you will bring (position requirements)
• Masters degree in information technology, applied computer science or computer engineering (or comparable degree).
• Experience with HPC cluster manager & job scheduling software (e.g. Slurm, PBS, etc)
• Administration experience with Linux OS (e.g. SLES/RHEL/CentOS/Ubuntu etc.).
• Good knowledge of the scripting language Bash and/or Python.
• Experience with Parallel file systems like GPFS/Lustre/Ceph/BeeGFS/Weka.
• Independent, result driven work, demonstrates ownership and accountability.
• English proficiency.
Relevant experience in some of the following technologies
• Experience with automation tools for configuration management (e.g. Ansible, Puppet, Chef) and revision control systems (e.g. Git)
• Experience with containers (Docker/ Singularity/Podman / Kubernetes).
• HPC system troubleshooting and support
• HPC cluster availability and performance monitoring with Icinga, Grafana, etc
• Experience with Ethernet, InfiniBand, RDMA network technologies
• CPU/GPU/memory/RAID/storage/Data Center technologies
• Knowledge of current technological developments/trends in area of expertise
What you can expect
• Exciting tasks in a dedicated, international team of scientists who are fully committed to ambitious research agenda
• Access to modern HPC systems and hardware
• A vibrant working environment with more than 200 international researchers from all over the world
• Family-friendly regulations
The position should be filled as soon as possible. Applications with the usual documents should be sent until 31 August 2023 - preferably in electronic form - to the Head of ML Cloud c/o. Kristina Kapanova, University of Tübingen, Maria von Linden Str 6, 72076 Tübingen, email: kristina.kapanova. Hiring is done by the Central Administration of the University of Tübingen. @uni-tuebingen.de
Severely disabled persons will be given preferential consideration if equally qualified. The University of Tübingen aims to increase the proportion of women in research and teaching and therefore invites applications from suitably qualified female candidates. The university is committed to equal opportunities and diversity. The position is divisible.
The Machine Learning in Medical Image Analysis Group invites applications for a
Student Research Assistant Position (40h per month)
Data scarcity is one of the major limiting factors preventing application of powerful machine learning algorithms to many medical applications beyond a handful of big public datasets. Cross-Domain Few-shot Learning (CD-FSL) offers the potential to exploit similarities between different medical image analysis datasets and leverage shared knowledge to learn previously unseen tasks more efficiently. However, CD-FSL is underexplored in medical image analysis. We recently released the MIMeta Dataset, the first medical image cross-domain few-shot learning benchmark, and started a challenge. With the L2L challenge we want to encourage the medical image analysis and machine learning communities to explore the potential of CD-FSL approaches in the promising application domain of medical image analysis, and to develop algorithms that are robust to the extremely high task and data diversity encountered in this domain. The L2L Challenge is an official MICCAI 2023 challenge. The International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) is the top conference in the domain of medical image analysis.
For more information visit our challenge website.
You will aid in building the evaluation system for the challenge and writing new features for the MIMeta and torchcross PyTorch libraries. Additionally you will help with technincal support for challenge participants.
- Good knowledge of Machine Learning and of Image Analysis/Computer Vision,
- Interest in working with medical imaging datasets,
- Interest in learning about and working on few-shot learning and/or meta-learning,
- Proficiency in Python, PyTorch and Git.
What we offer
- HiWi salary according to the standard rates of the University of Tübingen,
- A desk space in the Tübingen AI Research Building,
- Insights into an exciting and trending research field,
- The possibility of contributing to a scientific publication.
How to apply
If interested, please contact Stefano Woerner (stefano.woerner) and attach your CV and transcript of records to apply. Only currently enrolled students of the University of Tübingen can be considered for this position. @uni-tuebingen.de
The Schreiber Group at the University of Tübingen works on the physics of molecular and biological materials using X-ray and neutron scattering. A specialised sub-group is dedicated machine learning based data analysis of scattering and diffraction data. Currently we have several
PhD positions (m/f/d)
available. Candidates with experience or interest in neural networks and machine learning strategies to analyse scattering are especially encouraged to apply.
You should have good communication skills, attention to detail, and flexibility to work both independently as well as in a team. You should hold either a diploma/master degree in physics, physical chemistry, material science or have a background in computer science.
You will be part of challenging interdisciplinary projects that are integrated into major national and European research consortia such as the DAPHNE (DAta for PHoton and Neutron Experiments) NFDI consortium. We offer well-equipped laboratories, a highly collaborative international environment and affiliation with the Cluster of Excellence "Machine Learning: New Perspectives for Science" funded by the DFG and hosted at the University Tübingen. You will receive excellent training and for all our projects we offer the opportunity to perform research at international large-scale facilities (such as synchrotrons and neutron sources). Details on our research as well as publications and background information can be found at http://www.soft-matter.uni-tuebingen.de/machine_learning_XRR.html and http://www.soft-matter.uni-tuebingen.de/machine_learning_GIWAXS.html
The University of Tübingen has ~ 28,000 students and more than 500 years of academic tradition. It has national excellence status as is ranked in the top 100 universities worldwide. You will benefit from a variety of training opportunities and language courses as well as the university’s graduate academy. See also https://uni-tuebingen.de/en/excellence-strategy.
Applications should include a cover letter describing research interests, achievements, motivation and capabilities; curriculum vitae; academic certificates; names and email addresses of two professional references (e.g., current or previous research advisors). The opening will remain valid until the position is filled.
The positions are available immediately. Salary will be determined according to the German collective wage agreement in public service. Please send your application within one PDF file to firstname.lastname@example.org
The University aims to increase the proportion of women in research and teaching and therefore urges suitable qualified women scientists to apply. Qualified international researchers are expressly invited to apply. Severely disabled persons with equal aptitude will be given preferential consideration.