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Image Analysis Software Engineer

vor 1 Monat

region nordburgenland burgenland austria Seargin Vollzeit
  • Develop and Maintain the Fractal Platform
    • Contribute to the development and enhancement of the Fractal platform for large-scale bioimage analysis, ensuring it adheres to community-agreed open standards for image storage and visualization.
  • Convert Microscopy Image Formats
    • Implement efficient methods for converting diverse microscopy image formats into the OME-Zarr format to enable standardized storage and accessibility.
  • Integrate Image Processing Algorithms
    • Wrap image processing tasks such as Cellpose, optimizing performance for both CPU and GPU architectures.
  • Design Scalable Task Architectures
    • Contribute to creating flexible and scalable task architectures capable of running on HPC-like computing backends to handle large-scale image analysis.
  • Develop Web-Based GUIs
    • Build and refine user-friendly web-based graphical interfaces to start workflows, visualize data, and navigate data structures effectively.
  • Collaborate with Bioimaging Experts
    • Work closely with bioimaging experts and stakeholders to ensure that developed tools align with current and future imaging needs in bioimage analysis.
  • Implement Workflow Automation
    • Design automated workflows for large-scale bioimage analysis, minimizing manual intervention while ensuring reproducibility and accuracy.
  • Optimize Performance for Large Datasets
    • Identify and implement strategies to handle and process large bioimage datasets efficiently while maintaining data integrity.
  • Adopt and Contribute to Open Standards
    • Ensure that tools and workflows are compatible with community-agreed standards, and contribute to the development of these standards where applicable.
  • Enable Seamless Data Sharing
    • Develop features to facilitate seamless data sharing and integration across different imaging platforms and tools.
  • Perform Software Testing and Debugging
    • Conduct thorough testing and debugging of image analysis software to ensure reliability, accuracy, and performance across diverse datasets.
  • Document and Share Technical Insights
    • Create and maintain comprehensive technical documentation for developed tools and workflows, fostering knowledge sharing and collaboration within the bioimaging community.
Requirements
  • Degree in Relevant Field
    • A degree in bioinformatics, computer science, mathematics, or a related discipline is required to ensure a solid theoretical foundation for bioimage analysis and software development.
  • Proficiency in Scientific Python
    • Strong skills in Python, specifically for scientific computing, are essential for developing bioimage analysis software and implementing algorithms.
  • Experience with Relevant Frameworks and Tools
    • Familiarity with key frameworks and tools used in scientific Python development, such as NumPy, SciPy, TensorFlow, and others relevant to bioimage analysis, is a must.
  • Software Engineering Practices
    • Proficiency in software engineering tools and methods, including source control (GitHub/GitLab), CI/CD pipelines, and containerization (Docker), to ensure robust and reproducible software development.
  • Experience with Cloud or HPC Environments
    • Strong experience in designing scalable software solutions that can leverage high-performance computing (HPC) environments or cloud-based infrastructures for large-scale image processing tasks.
  • Mathematics and Statistical Knowledge
    • Solid understanding of mathematics, exploratory data analysis (EDA), and statistical methodologies is required to analyze bioimage data accurately and to implement advanced image processing algorithms.
  • Familiarity with Bioimage Analysis Techniques
    • Knowledge of or experience with common bioimage analysis techniques, including image segmentation, object detection, and feature extraction, particularly for microscopy data.
  • Proficiency in Data Storage Standards
    • Familiarity with data storage formats such as OME-Zarr, HDF5, or other bioimage standards, and understanding how to efficiently store and retrieve large image datasets.
  • Experience with GPU and Parallel Computing
    • Proficiency in utilizing GPU and parallel computing techniques to accelerate image processing tasks, particularly for handling large datasets.
  • Problem-Solving and Analytical Thinking
    • Strong problem-solving skills and the ability to think analytically to develop efficient and effective solutions for bioimage analysis challenges.
  • Cross-Disciplinary Collaboration Skills
    • Ability to work collaboratively with biologists, clinicians, and other stakeholders to understand requirements and implement solutions that meet the needs of diverse users.
  • Strong Communication Skills
    • Strong verbal and written communication skills to document software, write technical reports, and present findings or solutions clearly to both technical and non-technical audiences.
What we offer
  • B2B Contract
    • Employment based on a B2B contract.
  • Stable and Dynamic International Firm
    • Opportunity to work in a stable, dynamically developing international company.
  • Engaging Projects and Latest IT
    • Chance to participate in interesting projects and work with the latest information technologies.
  • Competitive Rates
    • Attractive remuneration rates offered.
  • Renowned International Projects
    • Involvement in the most prestigious international projects.
  • Multisport and Private Medical Care
    • Access to Multisport benefits and private healthcare services.
Nice to have
  • Experience with Image Processing on Biological Datasets
    • Previous experience applying image processing techniques specifically to biological datasets, with an emphasis on microscopy images, is highly preferred to ensure familiarity with the challenges of biological data analysis.
  • Development of User-Facing Applications
    • Experience in developing user-facing applications, either with Python (e.g., using frameworks like Flask/Django) or web technologies (JS/HTML/CSS), is beneficial for building intuitive interfaces for bioimage analysis.
  • Knowledge of Deep Learning Methods
    • Familiarity with deep learning methods, particularly those used for image segmentation, object detection, or other image processing tasks, is a plus for leveraging state-of-the-art techniques in bioimage analysis.
  • Knowledge of Classical Machine Learning Methods
    • Understanding classical machine learning methods, such as clustering, classification, and regression, which can be applied to bioimage data analysis for extracting insights or automating processes.
  • Experience with Image Processing Libraries and Frameworks
    • Experience with specialized image processing libraries, such as OpenCV, scikit-image, or Cellpose, is highly advantageous for efficiently developing and deploying image analysis workflows.

Work with us

Apply & join the team

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