Five founding roles

What you will do

• Manage the team, including scientific meetings when the CTO may not be available.
• Ensure all teams are committing the expected effort.
• Discuss the relevance of the scientific findings for each of the teams.

Required

• A degree of knowledge across the spectrum of the other positions.
• 8–12+ years of experience in computational drug discovery or ML.
• Proven leadership in multidisciplinary teams.
• Strong expertise in at least one of: deep learning, cheminformatics, or structural biology.
• Track record of delivering impactful projects.

What you will do

• Review the current ML engine.
• Expand the current ML engine with new features.
• Test the ML engine, optimising model hyperparameters and systematically assessing model performance, robustness and generalisability across varying experimental conditions, datasets and input configurations.
• Occasionally curate, preprocess and perform quality control on datasets.

Required

• Python, PhD-level.
• Machine learning general knowledge, PhD-level.
• PyTorch.
• CUDA-accelerated ML.
• PhD with focus on either computer science, bioinformatics, computational biology (or equivalent) – OR master degree + 2 years working experience for a big tech company.
• Some experience using cluster computing from remote access, e.g. SSH or similar.
• Some experience with multiomic data.

What you will do

• Expand our Graph Neural Network (GNN) engine.

Required

• PyTorch Geometric.
• Knowledge on training convolutional GNNs.
• Knowledge on multi-level GNNs.
• Hands-on experience in training GNNs for edge prediction tasks.
• CUDA-accelerated ML.
• Some understanding of biology or medicine.
• Understanding of graph theory and representation learning.
• PhD with focus on either computer science, bioinformatics, computational biology (or equivalent) – OR master degree + 2 years working experience for a big tech company.
• Some experience using cluster computing from remote access, e.g. SSH or similar.

What you will do

• Utilise state-of-the-art molecular docking software to establish an optimised prediction pipeline to predict drug-target interaction.

Required

• Hands-on experience in molecular docking, specifically diffusion models.
• Python, intermediate or above.
• Experience in protein-ligand binding prediction.
• Knowledge of protein databases: PDB, ChEMBL, UniProt, etc.
• Knowledge of structural prediction tools: AlphaFold, Rosetta, etc.
• Knowledge of RDKit.
• Some experience using cluster computing from remote access, e.g. SSH or similar.

What you will do

• Build upon and further develop our existing drug-repurposing infrastructure, integrating it with our core technology to identify and prioritise approved or investigational compounds predicted to modulate genes of interest with the desired direction of biological effect, including activation or inhibition.
• Occasionally curate, preprocess and perform quality control on datasets.

Required

• Hands-on experience working on drug effect predictions and drug-target interaction.
• PhD with focus on either computer science, bioinformatics, computational biology (or equivalent) – OR master degree + 2 years working experience for a big tech company.
• Knowledge of chemical databases and drug-focused databases.
• Python proficiency, intermediate.
• Knowledge of RDKit.
• Deep understanding of multiomic data: how to interpret and troubleshoot bulk-RNA sequencing, single-cell RNA sequencing, TMT proteomics, SNPs in genomics, etc.
• Some experience using cluster computing from remote access, e.g. SSH or similar.