Scientist Computational Structural Biology

Location: Town of Belgium

Scientist Computational Structural Biology page is loaded## Scientist Computational Structural Biology locations:
Gent, Belgium time type:
Full time posted on:
Posted Todayjob requisition :
REQ-4121
* Join us as we transform immunology and deliver medicines that help autoimmune patients get their lives back. argenx is preparing for multi-dimensional expansion to reach more patients through a rich pipeline of differentiated assets, led by VYVGART, our first-in-class neonatal Fc receptor blocker approved for the treatment of gMG, and with the potential to treat patients across dozens of severe autoimmune diseases.
** We are building a new kind of biotech company, one that maintains its roots as a science-based start-up and pushes our commitment to innovate across all corners of our business. We strive to inspire and grow our company, our partnerships, our science, and our people, because when we do, we deliver more for patients.
* 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 develop ability workflows.
** This position is based at our facility in Zwijnaarde, Belgium, and expects on-site presence at least 4 days a week.
**** 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.

Develop ability & AI Integration
* Extract and prepare structural features for AI/ML develop ability 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 develop ability 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., Alpha

Fold2, 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 develop ability challenges in biotherapeutics.
* Ability to work independently and collaboratively in a dynamic project-driven…