Postdoctoral Researcher - AI in Biological Image Analysis

About PHIO and the Role

PHIO is a life science technology company headquartered in Munich, focused on developing cutting-edge imaging and analytic systems for non-invasive, incubator-based live cell analysis. The company integrates proprietary lensfree imaging with advanced hardware, AI-driven software, and data interpretation to support life science research and biological process quantification through complex cell models.

The Postdoctoral Researcher position centers on advancing PHIO's AI capabilities in biological image analysis. This encompasses applied AI research, computer vision, and machine learning techniques tailored to live cell imaging, cell morphology, organoid examination, and multimodal biological data integration. The role involves working with diverse image datasets including lensfree, holographic, classical microscopy images, time-series, and assay metadata to develop robust, interpretable AI models translating complex imaging data into actionable insights.

Key Duties and Responsibilities

• Contribute to defining PHIO’s AI strategy by pinpointing significant AI applications in biological image analysis and life science data.
• Convert scientific and biological queries into reliable machine learning models suitable for validation and product development.
• Influence strategic decisions regarding data management, model design, evaluation methods, and future AI development.
• Create, train, and assess computer vision and machine learning algorithms for life science image analysis using frameworks like PyTorch or TensorFlow.
• Investigate multimodal AI techniques, synthetic data creation, data augmentation, and strategies for improving model generalizability.
• Design reproducible machine learning experiments, including data pipelines and evaluation procedures.
• Develop well-documented research prototypes for validation and transition to product engineering teams.
• Support the maturation of AI research outputs into production-level features through collaboration with software engineers.
• Maintain scientific rigor while delivering practical, timely AI solutions in a startup environment.
• Work collaboratively with teams across biology, software, hardware, product development, and leadership.
• Contribute to technical documentation, facilitate knowledge sharing, and engage in select scientific customer collaborations.

Candidate Requirements

• PhD in machine learning, computer vision, bioinformatics, computational biology, physics, mathematics, computer science, or a related quantitative discipline with a minimum of two years' relevant postdoctoral or industry experience; or a Master’s degree with at least five years of substantial experience in applied machine learning, computer vision, biological image analysis, or AI for life sciences.
• Deep expertise in image-based machine learning with hands-on experience handling complex biological or biomedical image datasets.
• Proficient in Python programming and experienced with deep learning frameworks such as PyTorch or TensorFlow.
• Ability to independently conceptualize scientific problems and develop practical, testable machine learning solutions.
• Demonstrated capability to take technical ownership of AI or computer vision projects, including experiment design, evaluation criteria development, model review, and cross-functional coordination.

Personal Attributes

• Proactive, hands-on approach with enthusiasm for working directly with data, code, and AI models.
• Organized, pragmatic, and scientifically methodical working style focused on producing robust, well-documented prototypes.
• Comfortable dealing with ambiguous biological challenges, incomplete data, and shifting priorities typically found in startup environments.
• Excellent communication skills enabling effective collaboration across AI, biology, software engineering, product, and leadership teams.

Additional Information

• This full-time position is based in Munich, Planegg, or Martinsried with some flexibility for hybrid work depending on project needs and fit.
• Applicants should submit a current CV highlighting relevant projects and impact, along with evidence of their work such as GitHub repositories, Google Scholar profiles, portfolios, or publication lists.
• A brief message (2–3 sentences) expressing enthusiasm for applying AI to live cell imaging is requested instead of a formal cover letter.