Senior Structural Bioinformatics Researcher (Machine Learning)

Employee

At PUXANO, we are revolutionizing structure-based antigen design by integrating biotechnology, nanotechnology, and AI to unlock previously inaccessible protein targets. Our proprietary platforms accelerate everything from sequence analysis to automated high-resolution structure determination via cryo-EM, empowering global pharma and biotech partners to develop novel vaccines, antibodies, and small-molecule therapeutics for membrane proteins and protein complexes.

We are expanding our computational team in Ghent (soon Kortrijk), Belgium, to pioneer the next generation of intelligent, structure-guided workflows.

Position Overview

We are seeking a motivated Senior Bioinformatics Researcher (PostDoc level or 7+ years of experience) with a strong background in Structural Bioinformatics and Machine Learning (ML) to lead our predictive modeling pipelines. In this role, you will develop novel target identification and protein design workflows, closely collaborating with our experimental structural biology team.

Note on Protein Design/Structure Prediction: Direct experience with de novo protein design or predictive algorithms (e.g., ProteinMPNN, RFdiffusion, AlphaFold) is a major plus, but not a strict requirement. We prioritize candidates who can apply advanced ML/DL methodologies to structural biology data to solve complex challenges.

Key Responsibilities

Data Analysis: Integrate and analyze multi-omics datasets from public and proprietary sources to identify, filter, and prioritize high-value protein targets for structural pipelines.

ML Model & Platform Development: Design, train, and deploy ML architectures for processing 3D macromolecular structures and protein-ligand complexes.

Advance the procondor Platform: Optimize database structures and integrate multi-format metadata and sequence-level protein features into our proprietary software.

Hybrid AI Workflows: Develop pioneering pipelines that combine sequence-based predictions with experimental data (e.g., cryo-EM, epitope residues), aligning with PUXANO’s vision of automated structural resolution.

Cross-Functional Collaboration: Work closely with nanoengineers, structural biologists, and wet-lab biochemists to translate in silico predictions into robust experimental data.

Data Management & HPC Scaling: Preprocess and structure large-scale structural datasets, leveraging High-Performance Computing (HPC) frameworks to scale model training.

Qualifications & Skills

Required Education & Experience

Ph.D. in Structural Bioinformatics, Computational Biology, Biophysics, Data Science/Computer Science (with a biological focus), or a related quantitative field.

3+ years of experience (industry or academic postdoc) applying ML algorithms to macromolecular structural biology data.

Technical Competences

Deep Learning Frameworks: Expertise in PyTorch (or similar modern ML/DL stacks).

Structural Toolkits: Hands-on experience with molecular modeling packages (e.g., Biopython, PyMOL, Chimera, MDAnalysis, OpenMM, Rosetta).

Software Engineering: Strong Python skills, data wrangling (Pandas, NumPy, SciPy), and database design for efficient storage/retrieval.

HPC Familiarity: Experience with High-Performance Computing (HPC) environments (e.g., EuroHPC).

Preferred Qualifications (“Plus” Factors)

Experience implementing or fine-tuning algorithms for de novo protein design/structure prediction (e.g., AlphaFold 2, RoseTTAFold, ESMFold, RFdiffusion).

Familiarity with open-source computational consortia (e.g., OpenFold, Genie) or EuroHPC frameworks.

Experience with automated image processing software for single-particle cryo-EM analysis.

What We Offer

High-Impact Role: Shape a tech-driven platform at the intersection of AI, nanotech, and biology.

Cutting-Edge Resources: Access to world-class datasets and advanced compute infrastructure.

Collaborative Culture: A fast-paced startup ecosystem with a flexible, scientific mindset.
Competitive Package: Salary and benefits in Flanders’ vibrant biotech hub.