Summary
MBZUAI is seeking a Research Engineer for its Multiscale Biology Visualization AI project in Abu Dhabi. The role involves building deterministic, parameterized geometry-generation pipelines for cellular structures and integrating their outputs with AI, visualization, and rendering systems. The engineer will develop automated tests and validation metrics and contribute to documentation and reproducible research practices. A master’s degree in computer science, computational geometry, computer graphics, or a related field is required; a PhD is preferred, along with experience in computational geometry, mesh processing, procedural modeling, or simulation.
The posting
As published on mbzuai.ac.ae. Pulled from the employer's site and shown as they wrote it.
MBZUAI is seeking a Procedural Geometry & Computational Modeling Research Engineer to support a Multiscale Biology Visualization AI project. The role focuses on designing and implementing algorithm-driven, deterministic geometry generators for subcellular structures, enabling biologically plausible and computationally efficient representations of cellular components. The Research Engineer will develop parameterized, testable shape synthesis pipelines that integrate upstream AI-generated biological outputs and produce standardized geometry assets for downstream visualization and rendering systems. The position requires strong expertise in computational geometry, structured algorithm design, and reproducible research practices within a fast-paced, interdisciplinary research environment.
Key Responsibilities
Procedural Geometry Modeling
- Design and implement procedural, algorithm-driven generators for subcellular geometry.
- Develop deterministic, seed-controlled, parameterized shape synthesis pipelines that are testable and reproducible.
- Create geometric representations for a broad set of cellular structures (target scope includes approximately 35 organelles).
- Balance biological plausibility, computational efficiency, and visual fidelity in geometry generation.
Pipeline Integration & Architecture
- Define schema- and configuration-driven interfaces to transform upstream biological AI model outputs into standardized geometry outputs.
- Prepare generated geometry for seamless integration into the visualization and rendering stack.
- Ensure interoperability across modeling, rendering, and AI-facing components.
Quality Assurance & Reproducibility
- Develop validation metrics and automated tests for topology correctness, realism, reproducibility, and performance.
- Contribute to code quality, structured documentation, and reproducible research practices.
- Maintain clear version control and collaborative development workflows.
Collaboration & Research Contribution
- Collaborate closely with visualization engineers, AI researchers, computational biologists, and modeling specialists.
- Participate actively in technical design discussions and solution development.
- Operate effectively in a fast-paced, agile research environment with evolving requirements.
Academic Qualifications Required
- Master’s degree in Computer Science, Computational Geometry, Computer Graphics, or a related field.
- A PhD in a relevant discipline will be preferred.
Professional Experience Required
Essential:
- Demonstrated experience in computational geometry, mesh processing, procedural modeling, or simulation.
- Strong algorithmic skills with proficiency in Python (experience in C++, Rust, or Node.js is advantageous).
- Solid understanding of geometric data structures, topology, and polygonal mesh generation pipelines.
- Strong debugging skills and advanced algorithmic problem-solving ability.
- Experience working in collaborative development environments using tools such as Git and modern communication platforms.
Preferred:
- Experience with biological imaging, cellular morphology, or structural biology data.
- Knowledge of geometry libraries and polygonal mesh optimization for real-time or high-performance applications.
- Exposure to differentiable geometry or machine learning–guided geometry workflows.