Job Informationen

Location: Basel, hybrid Workload: Full-time We are seeking a Data Scientist or Data Science Associate (0–4 years experience) to join the DeepIR computational team and support machine learning model development and inference for novel antibody and TCR design. In this role, you will develop and apply advanced computational models, including graph neural networks (GNNs), protein language models (pLMs), and structural modeling of protein–protein interactions. These computational workflows will be used to understand patterns of antibody-antigen specificity or TCR-pMHC binding. You will work in close partnership with experimental scientists, using real-world data to validate and refine computational designs. Your tasks: Develop and optimize ML models: Design, train, and fine-tune machine learning models (e.g. GNNs, deep neural networks, protein language models) to predict and design protein–protein interactions, such as antibody-antigen and TCR-peptide binding. Model inference and deployment: Support the inference pipeline by implementing robust code to apply trained models for virtual screening and de novo protein design predictions. Ensure efficient deployment of models on HPC or cloud infrastructure for large-scale runs. Data analysis and integration: Work with high-throughput datasets (e.g. sequence data, structural data from PDB) to construct protein interaction graphs and featurize molecules. Analyze model outputs to identify promising antibody/TCR candidates and generate insights into key interaction features. Collaboration with experimental team: Interact closely with laboratory scientists to incorporate experimental feedback. Plan in silico experiments in tandem with wet-lab validation – for example, use experimental binding or structural data to improve models, and suggest new designs for lab testing. Research and innovation: Stay up-to-date with the latest computational immunology and protein design research. Prototype novel approaches (such as protein language models, diffusion models, or improved GNN architectures) and assess their potential to enhance the DeepIR design platform. Communication: Present findings and progress in team meetings. Contribute to publications or reports as needed, clearly communicating complex computational results to interdisciplinary team members. Your profile: Education & Experience: Master’s degree or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, or a related field. 0–4 years of relevant experience (industry or research). Machine Learning Expertise: Hands-on experience with machine learning/deep learning, including proficiency in Python and libraries such as PyTorch or TensorFlow. Ability to develop and debug models and data pipelines. Graph Neural Networks: Familiarity with graph-based learning techniques. Ideally experience using GNN frameworks (e.g. PyTorch Geometric, DGL) for structural biology or network data, and understanding of how GNNs can model molecular interactions. Computational Biology Knowledge: Basic understanding of protein biology and structural modeling (e.g. concepts of antibodies, TCRs, amino acid properties, PDB files). Experience in handling biological datasets or working on problems like protein structure prediction, molecular dynamics, or sequence analysis.

Benötigte Skills

• Support
• Testing
• CLOUD
• Python
• Machine Learning
• Bioinformatik
• Master