Slurm Experts in Germany
for high-performance workloads, matched in minutes with vetted freelancersHire experts who manage Slurm clusters, tune job scheduling and connect HPC workloads with Linux, MPI and GPU resources. FRATCH matches you quickly and precisely with vetted, available freelancers who fit your technical needs.
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.
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
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.
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 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Germany are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge 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 19 Sep 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
Slurm at a glance
Slurm, short for Slurm Workload Manager, is an open-source workload manager for Linux-based high-performance computing clusters. It allocates compute resources, queues jobs, starts workloads and tracks their status across shared infrastructure. Companies use it to run scientific simulations, data processing and machine learning workloads without unmanaged contention.
Scheduling and control
Slurm separates users from individual compute nodes. Specialists define partitions, quality-of-service rules, priorities, reservations and limits so workloads receive resources according to business or research needs. Its controller and compute-node services support job arrays, dependencies, preemption, accounting and fair-share scheduling.
Ecosystem and tooling
Slurm expertise usually spans the surrounding HPC stack as well as the scheduler itself:
- Configure slurmctld, slurmd, partitions and node states
- Integrate MPI, CUDA, GPUs, containers and parallel filesystems
- Automate submissions with Bash, Python and workflow tools
- Monitor jobs with accounting, logs and cluster dashboards
Where companies use it
Slurm runs in research computing, universities, engineering, life sciences, financial modelling and industrial simulation. In Germany, teams may use it for workloads that combine local data, dedicated on-site clusters and cloud capacity. Freelance specialists help connect scheduling policy with security, storage, networking and user workflows.
When outside expertise helps
Companies often seek external support during a new cluster rollout, a migration from another scheduler or a major workload change. Other signals include long queue times, idle nodes, unreliable GPU allocation, unclear chargeback data or repeated job failures. A specialist can audit configuration, improve throughput and document operating procedures.
What strong professionals deliver
Strong Slurm professionals understand both cluster operations and the workloads placed on them. They can explain scheduling trade-offs, test changes safely and trace failures across the scheduler, operating system, network, storage and application layers. For remote work, clear documentation and reliable observability matter; on-site collaboration can help when hardware, access controls or facility processes are involved.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Slurm.
Slurm is used to schedule and manage workloads across high-performance computing clusters. It assigns CPUs, memory, GPUs and other resources, then queues, starts and monitors jobs according to configured policies.
Slurm Workload Manager is commonly weighed against systems such as PBS Professional, OpenPBS, LSF and Kubernetes. Slurm is especially suited to batch-oriented HPC workloads, while Kubernetes focuses more on long-running containerized services and application orchestration.
A strong Slurm specialist often also understands Linux administration, Bash or Python automation, MPI, CUDA, GPU configuration, high-speed networking and parallel filesystems. Experience with containers, workflow systems and monitoring is useful when clusters support varied research or production teams.
The right level depends on the scope. A configuration review may need focused cluster knowledge, while a new HPC environment requires someone who can design partitions, security, accounting, failover and integration with storage and workload tools.
Slurm can often be configured and reviewed remotely through controlled access, logs, documentation and test environments. On-site work may be valuable for hardware installation, restricted facilities or changes that require direct coordination with local infrastructure teams in Germany.
Ask for concrete examples of cluster sizes, workload types and scheduling problems they have handled. A capable Slurm professional should explain why a configuration choice was made, how changes will be tested and which metrics will confirm improvement.
Slurm supports GPU scheduling through configured resources such as GRES and can coordinate exclusive or shared GPU allocation. A specialist should also understand CUDA, device visibility, container runtimes, job arrays and the storage or data-transfer demands of machine learning workloads.
Simple Linux Utility for Resource Management is the expanded name behind the Slurm acronym and is often mentioned when discussing its history. The project is now generally known as Slurm or Slurm Workload Manager, and professionals should recognize all of these terms in technical documentation.
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 477 € 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
