Slurm Experts in Germany
matched in minutes by AIHire experts who configure Slurm clusters, automate HPC job workflows and integrate workload scheduling with cloud or on-premises infrastructure. FRATCH connects you with vetted, available freelancers through fast, precise AI matching.
Meet FRATCH Experts in Germany, who have recently used Slurm
Amr A.
Last position:
Machine Learning Engineer at German Research Center for Artificial Intelligence (DFKI)
- Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
- Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
- Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
- Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
- Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.
Ebenezer N.
Last position:
Applied Data Science & AI Bootcamp
- Prototyped LLM/RAG document assistant; trained transcriptomics and proteomic data; used Git/Docker for reproducibility.
- Strengthened ML fundamentals applicable to omics (feature engineering, validation, leakage control).
Noushiq M.
Last position:
Projects at Institute for Intelligent Systems
- Evaluation and analysis of camera-based traffic light and sign recognition system on various LLM-based autonomous driving systems (LMDrive, BEVDriver)
- Implemented VLM based traffic notice instruction generation unit for closed-loop autonomous driving system which alerts driver in unforeseen driving incidents
- Developed independent LLM-based local chatbot with Llama, DeepSeek and Qwen including MLflow evaluation framework
Yasaman N.
Last position:
Research Data Scientist at RWTH Aachen University
- Led large-scale data analysis on high-volume event data, applying statistical modeling and time-series methods to detect weak signals in noisy environments
- Designed and maintained scalable Python data pipelines for real and simulated datasets, optimizing signal detection and background estimation at scale
- Applied advanced statistical inference techniques (likelihood-based modeling, hypothesis testing) to support quantitative decision-making
- Deployed data-processing workflows on HPC clusters using Slurm, enabling parallelized analysis and large-scale batch execution
- Built reproducible, version-controlled data workflows using Python, Bash, and Git to ensure reliability and traceability of results
- Translated complex experimental data into actionable insights, enabling quantitative decision-making
Evangelia C.
Last position:
Postdoctoral Researcher & Data Steward at Technical University Darmstadt (Müller-Plathe group)
- Developed and applied a symbolic regression scheme to explore and model complex relationships in liquid viscosities across their phase space, providing predictive insights into material properties.
- Contributed to the improvement of hybrid Particle-Field models through data-driven diagnostics and model enhancement strategies.
- Lead interdisciplinary collaboration with experimental physicists for the investigation of the molecular mechanisms behind water-based inks.
- Managed research data workflows to ensure public accessibility and institutional archiving of simulation data, code scripts, and inputs, directly supporting research transparency and reproducibility.
Subodh K.
Last position:
Student Assistant at University of Indore
- Collaborated on a pivotal project involving Quasi-Monte Carlo (QMC) methods for Bayesian optimization in PDEs, contributing to the implementation and focusing on uncertainty quantification.
- Addressed Bayesian inverse problems governed by PDEs, conducting detailed analysis for double integration problems using two approaches: a full tensor product and a sparse tensor product.
- Improved computational efficiency by developing and optimizing QMC-based algorithms, resulting in significant enhancements in the robustness of uncertainty quantification processes.
- Conducted extensive optimization and validation of algorithms, leading to more reliable and precise predictions in PDE models.
Talha T.
Last position:
API Development for Advanced CDS Analytics at Academic Project
- Developed APIs for advanced credit default swap analytics supporting both MongoDB and file-based workflows
Nhu Loc Thuy T.
Last position:
Ph.D. Researcher in Quantitative Genetics & Computational Biology at University of Cologne (CEPLAS – Cluster of Excellence in Plant Sciences)
- Generated and analysed large-scale RNA-seq data (>800 samples) using R, Python, and high-performance computing (HPC/Linux) systems.
- Integrated multi-omics data (genomic, transcriptomic, and phenotypic); applied Bayesian approaches and machine learning to study gene expression variation and inheritance of complex traits.
- Mentored B.Sc. and M.Sc. students in experimental design, programming in R/Python/Bash, biostatistics, data visualisation and scientific presentations.
Emre A.
Last position:
Development of a software solution for archiving and a GenAI-based Q&A tool
- Banking industry
- Configuration and setup of a Google Cloud project with the Vertex AI API, Vertex AI Matching Engine, and Google Cloud Storage
- Development of Python services for ingesting and analyzing data in various formats using the Q&A API
- Containerization of components and implementation of Kubernetes configurations
- Development of a .NET service and a React frontend for data-driven capture with dynamic process steps
- Refactoring and functional extension of existing code to meet new requirements in ingestion and reconciliation
- Technologies: .NET Core, Moq, C#, PostgreSQL, Entity Framework, Avro, Python, Flask, Flask unittest, Pip, LangChain, RabbitMQ, REST, Jupyter, Docker, GitHub, kubectl, Google Vertex AI, Google Vertex AI Matching Engine, BigQuery, Google Gemini, React, Bootstrap, Vite, Vitest, npm
Discover over 15,000 top freelancers
Statistics of experts using Slurm
Aggregated from the professional profiles of matched freelancers.
Experience
9 years
Position duration
2.3 years
Positions per freelancer
7
Top business areas
Research and Development, Information Technology, Product Development
Top industries
Education, Agriculture, Aerospace and Defense
Certification focus areas
Research and Development, Business Intelligence, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
38%
Certifications per freelancer
1
Most common languages
German, English, Arabic
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Germany using Slurm
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Slurm experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Education (89%)
- Agriculture (33%)
- Aerospace and Defense (22%)
- Automotive (22%)
- Biotechnology (22%)
- Chemical (22%)
- Banking and Finance (22%)
- Information Technology (22%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Slurm does
Slurm, short for Simple Linux Utility for Resource Management, is an open-source workload manager for high-performance computing clusters. It allocates compute resources, queues jobs, enforces policies and tracks usage across Linux-based systems. Companies use it to run simulations, research workloads, data processing and machine learning at scale.
Core cluster work
A Slurm environment combines a controller, compute nodes, partitions and a database-backed accounting layer. Specialists configure slurmctld, slurmd, sinfo, squeue and sbatch, then define queues, priorities, limits and reservations. They also design job scripts that request CPUs, memory, GPUs, wall time and node features accurately.
Ecosystem and tooling
Slurm connects with the wider HPC stack rather than operating as an isolated scheduler.
- Integrate Open OnDemand for browser-based cluster access
- Configure GPU scheduling for CUDA and machine learning workloads
- Connect Slurm accounting with MariaDB and reporting tools
- Automate provisioning with Ansible, Terraform or cloud services
- Support MPI, Singularity or Apptainer and parallel file systems
When companies need expertise
Freelance specialists are useful when a cluster is being designed, migrated or brought under reliable operational control. Common triggers include poor queue performance, incorrect resource requests, failed jobs, GPU contention and a move from legacy batch systems. In Germany, teams may also need professionals who can collaborate remotely while joining on-site workshops for infrastructure or research environments.
Strong professional skills
The best Slurm professionals understand scheduling policy as well as Linux operations. They can diagnose pending jobs, node health, cgroups, memory pressure and network or storage bottlenecks without masking the underlying cause. Strong deliverables include tested configuration, clear runbooks, reproducible job templates, monitoring dashboards and a safe rollback plan.
Planning a Slurm project
Start by defining workload types, resource shapes, service levels and the systems that must connect to the cluster. Clarify whether the environment is on-premises, hosted in a cloud or hybrid, and identify security, data locality and access requirements. A qualified specialist should explain trade-offs between throughput, fairness, utilization and operational simplicity before changing production settings.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Slurm.
Slurm schedules and manages batch jobs across compute clusters. It is commonly used for scientific simulations, genomics, rendering, large-scale data processing and GPU-based machine learning. It controls resource access so many users and workloads can share infrastructure predictably.
Slurm is designed primarily for HPC batch scheduling, tightly coupled parallel jobs and specialized compute hardware. Kubernetes focuses on running containerized services and applications, although the two can coexist when teams need both cluster scheduling and cloud-native orchestration.
A strong Slurm freelancer usually works comfortably with Linux, Bash, Python, networking, storage and infrastructure automation. MPI, CUDA, Apptainer, monitoring, databases and cloud infrastructure are also valuable when the cluster supports research or machine learning workloads.
The right level depends on the scope. A small queue or job-script improvement may need focused scheduling knowledge, while a new cluster requires experience with controller design, authentication, accounting, high availability, storage and failure recovery. Ask for examples of comparable workloads rather than relying on a generic background.
Slurm administration and configuration can often be completed remotely through secure access, documentation and scheduled testing. On-site collaboration may still help with physical cluster deployment, networking or workshops, and German-language communication can matter when several local infrastructure teams are involved.
Slurm is a strong fit when workloads are batch-oriented, compute-intensive or dependent on MPI, GPUs and shared cluster policies. Companies should compare it with alternatives such as Kubernetes, PBS Professional or IBM Spectrum LSF based on workload behavior, existing skills, licensing needs and the surrounding infrastructure.
Look for measurable operational evidence such as predictable queue behavior, correct resource isolation, clear job states and reliable recovery from node or controller failures. A capable Slurm professional should provide tested configuration, monitoring, runbooks and an explanation of how scheduling decisions affect users and utilization.
Slurm was originally known as the Simple Linux Utility for Resource Management, and the project is commonly referred to by its acronym. People may also search for Slurm workload manager or HPC scheduler when looking for specialists, tooling and implementation guidance.
The average hourly rate of freelancers in Germany who have used Slurm in their recent projects is 60 €, which corresponds to a daily rate of about 478 € based on an 8-hour working day.
Of the freelancers in Germany who have used Slurm in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 38% hold a doctorate.
On average, freelancers in Germany who have used Slurm in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used Slurm in their recent projects are German (100%), English (100%), and Arabic (22%).
The most common industries among freelancers in Germany who have used Slurm in their recent projects are Education (89%), Agriculture (33%), and Aerospace and Defense (22%).
The most common business areas among freelancers in Germany who have used Slurm in their recent projects are Research and Development (100%), Information Technology (44%), and Product Development (44%).
Main locations of FRATCH Experts, who have recently used Slurm
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Berlin Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
