Senior Scientist Translation Research - AI Protein Structure & Design

Senior Scientist Translation Research - AI Protein Structure & Design

VIB

Ghent, Belgium

About the role

We are seeking a Senior Computational Biology Scientist in AI Protein Structure Prediction and Design to help build and translate AI‑driven capabilities for biologics design and protein engineering. The role combines protein structural insight with hands‑on ML development: adapting and applying state‑of‑the‑art structure prediction and design frameworks, training/fine‑tuning models, and running scalable computational campaigns.

Key responsibilities

• Design and execute in silico protein and biologics engineering campaigns guided by structural biology and biophysical principles.
• Translate computational outputs into biologically meaningful insights relevant for protein/antibody design in support of VIB’s valorization projects.
• Train, fine-tune, benchmark and apply state‑of‑the‑art AI methods for protein structure prediction and generative protein design (e.g. AlphaFold, OpenFold, Boltz, Chai).
• Develop robust, reproducible and reusable Python code for model training, inference, and large‑scale computational experiments.
• Run and manage high‑throughput workloads on HPC or cloud infrastructure, including parallelization, traceability, reporting and workflow orchestration through Nextflow.
• Collaborate with data/AI engineers, computational biologists, and experimental partners in both academia and industry to advance translational projects towards partnerships, or venture creation.

Your profile

• PhD with 5+ years of postdoctoral and/or relevant industry experience, demonstrating scientific independence, technical depth, and a strong track record of applied research in Computational Biology, Structural Biology, Protein Engineering, Machine Learning, or a closely related field.
• Strong understanding and demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering.
• Hands‑on experience with common machine learning/deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data.
• Solid Python programming skills, with experience building maintainable and portable code for scientific workflows with Nextflow (or comparable).
• Practical experience running large‑scale computational workloads on HPC or cloud infrastructure (e.g. job schedulers, parallelization, data latency).
• Ability to critically interpret computational protein designs in a biophysical and therapeutically relevant context (e.g. binding, stability, developability).
• A pragmatic, collaborative mindset with a keen interest in biotechnology translation, platform building, valorization projects and venture building.

Desirable/Plus

• Experience working in lab‑in‑the‑loop settings, iterating computational designs with experimental or wet‑lab validation and feedback.
• Hands‑on experience with biophysics‑guided modelling and force‑field‑based approaches (e.g. Rosetta, FoldX, or related methods), ideally in combination with ML models.
• Prior involvement in computational antibody design platform building for biotech/pharma.