Scientist Computational Structural Biology

Scientist Computational Structural Biology

argenx

Ghent, Belgium

This role should strengthen our Discovery and Preclinical teams by combining structural biology, computational tooling, and AI/ML assisted feature predictions to accelerate and improve our antibody development. With a primary focus on antibody engineering, this person will deliver structural insights, implement and configure computational tools, and contribute to predictive developability workflows.

Key responsibilities

Structural Computational Biology Support

• Identify, evaluate, and implement computational structural biology tools with the potential to facilitate antibody engineering efforts;
• Interpret structural data and advise on methodology improvements;
• Support wet‑lab colleagues with interpretation of structural predictions.

Developability & AI Integration

• Extract and prepare structural features for AI/ML developability models;
• Benchmark and contribute to algorithms for predicting biophysical properties;
• Build curated datasets integrating structural, sequence, and assay-derived attributes;
• Keep track of and, if applicable, implement new developments and tools regarding structure-based antibody engineering and therapeutic antibody developability predictions.

Cross‑Functional Collaboration

• Provide training and workflow guidance for structural tools;
• Collaborate with wet-lab scientists to validate computational predictions experimentally;
• Communicate scientific results to diverse stakeholders, including project leads.

Competencies

Core Competencies:

• Scientific problem-solving: generate hypotheses from structural data and select the appropriate computational approaches. Critically evaluate the outcome and assess how this impacts the research question;
• Innovation & algorithm scouting: identify, benchmark, and evaluate new structural and biophysical prediction tools;
• Collaboration: work effectively across wet‑lab, computational, and AI‑focused teams;
• Communication: translate complex structural predictions into clear, actionable insights.

Technical Competencies:

• Protein structure prediction and design (e.g., AlphaFold2, Boltz2 and similar, Rosetta), docking and interface analysis. Experience with molecular dynamics simulations is a plus;
• Strong Python skills and experience with reproducible workflows. Experience with version control (Git), testing, and collaborative development practices for scientific code;
• Familiar with FAIR data principles and computational infrastructure optimization.

Profile

• PhD in Structural Biology, Computational Biology, Biophysics, or related field;
• Strong background in structural modelling and analysis. Relevant experience with antibodies is a strong added value;
• Experience integrating computational predictions with wet‑lab validation;
• Programming proficiency (Python), workflow automation experience and hands-on experience with containerization technologies (Docker, Podman);
• Familiarity with AI and machine learning concepts as applied to biological data, knowledge about developability challenges in biotherapeutics;
• Ability to work independently and collaboratively in a dynamic project-driven environment, with excellent communication skills and a proactive mindset;
• A results-driven approach, motivated by the opportunity to make a tangible impact on the discovery and development of novel therapeutics, and a passion for seeing scientific concepts translated into real-world applications;
• Proactive, curious, and eager to bridge computation and experimentation;
• Excellent written and verbal communication. Ability to translate complex structural biology concepts for non-expert audiences;
• Thrives in fast‑paced, collaborative environments.