Two Open Positions in Computer Vision and Cloud Infrastructure for Biomedical Applications
Pubblicato il 15-09-2026 - Università di Trento in Trento
Two Open Positions in Computer Vision and Cloud Infrastructure for Biomedical Applications within the UniTrento EnjoyTheLab Project The EnjoyTheLab project, led by Luca Marchetti and Emiliano Biasini at DCIBIO, University of Trento, is seeking candidates for two positions under six-month co.co.co. contracts ( contratti di collaborazione coordinata e continuativa ).
EnjoyTheLab is a no-code platform that enables biomedical researchers to independently perform advanced bioinformatics analyses.
Currently focused on genetic data analysis, the web application aims to expand its capabilities by integrating computer vision solutions for the automated processing of laboratory images.
Contract and location
- Number of positions: 2
- Contract type: Co.co.co. ( contratto di collaborazione coordinata e continuativa )
- Duration: 6 months for each position
- Location: University of Trento, Italy. Some level of remote working may be considered.
- Expected start date: October/November 2026
How to apply
Please send your full application by email to (email hidden), (email hidden), and (email hidden), specifying in the subject line the position for which you are applying.
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Position 1 — Computer Vision & Deep Learning Researcher (Biomedical Imaging)
We are looking for an ambitious researcher or engineer eager to contribute to scientific and technological innovation in a rapidly growing biomedical field.
The scientific and technological innovation you will drive
Cutting-edge image quantification: Develop innovative algorithms for automatic lane detection,
faint protein band segmentation, and molecular weight ladder alignment.
Biomedical vision and signal processing: Train and fine-tune deep learning models using PyTorch or TensorFlow to analyze complex gel and Western Blot images, addressing challenging cases such as high optical background noise, sample distortion, and signal saturation.
Benchmarked quantification engines: Develop densitometry routines, including intensity profiling and baseline estimation, to advance automated signal analysis beyond established tools such as ImageJ/Fiji.
Core competencies
- Advanced knowledge of computer vision and image processing tools, including OpenCV, Scikit-image, and Albumentations.
- Experience with 1D and 2D signal-processing methods, including intensity profiling and peak detection.
- Experience with PyTorch or TensorFlow, particularly for object detection and instance segmentation.
- Familiarity with relevant evaluation metrics, including mAP, IoU, and the Dice coefficient.
- Experience with Python, Git, and Docker.
- Experience with annotation tools such as CVAT, Roboflow, or Label Studio.
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Position 2 — Infrastructure & DevOps Engineer (Django & Cloud Infrastructure)
We are looking for a software engineer eager to transform an existing prototype into a resilient, production-ready architecture.
The engineering challenge: from prototype to production
Production-grade stack containerization: Transition the core application stack—Django, PostgreSQL, Redis, Celery, and Nginx—from early local builds to standardized, containerized staging and production environments using Docker and Docker Compose.
Scalable asynchronous architecture: Strengthen the task-queue architecture so that computationally intensive bioinformatics workloads can run efficiently on decoupled worker pools using Celery and Redis, with production-ready rate limiting, failure retries, and Flower monitoring.
Cloud readiness and storage abstraction: Abstract local storage dependencies using MinIO/S3 APIs and develop Infrastructure as Code solutions with Terraform or Ansible to support the transition to public cloud infrastructure.
CI/CD and hardening: Develop automated testing and deployment pipelines using GitHub Actions and implement reverse-proxy security, SSL management, and firewall configurations.
Core competencies
- Hands-on experience with Python, Django, PostgreSQL, Celery, and Redis.
- Experience with Docker, Docker Compose, and Nginx or Traefik.
- Linux administration skills, particularly with Ubuntu or Debian, and experience with Bash scripting.
- Experience integrating MinIO or AWS S3 APIs.
- Knowledge of SSH security and basic Infrastructure as Code practices using Terraform or Ansible.
- Experience transforming functional code into scalable, fault-tolerant infrastructure designed for zero-downtime operation.