Scientist Applied Machine Learning Sevilla
Role details
Job location
Tech stack
Requirements
large-scale biological and imaging datasets, including structural and functional MRI* Create scalable and reproducible ML pipelines on cloud and/or HPC environments* Collaborate closely with computational and experimental scientists; contribute to or lead projects depending on your experience* Exchange ideas and best practices with teammates to collectively raise the quality of the work* Communicate insights clearly, ensuring your work informs both scientific understanding and strategic decisionsWhat you bring* Ph.D. in Machine Learning, Bioinformatics, Computer Science, Statistics, Applied Mathematics, Physics, Electrical Engineering, or a related field* Strong hands-on experience with modern machine learning and deep learning methods; exposure to foundation models or multimodal learning is a plus* Proficiency in Python and experience with PyTorch and the broader scientific Python ecosystem* Experience with cloud platforms (AWS, Azure, GCP) and/or HPC systems (e.g., SLURM)* Familiarity with Docker, Git, and reproducible research practices* Experience working with complex, large-scale datasets; background in biological, clinical, or imaging data is advantageous* Evidence of impactful work (publications, projects, or applied contributions), appropriate to your career stage* Ability to work independently while thriving in collaborative, multidisciplinary settings* Clear and effective communication skills in English* Genuine interest in applying AI to biomedical and healthcare challengesDesirable extras* Familiarity with MRI and neuroimaging data (e.g., structural MRI, fMRI, diffusion imaging)* Experience with foundation model training strategies (masked, contrastive, autoregressive) and multimodal architectures* Experience with LLMs and related tools (e.g., HuggingFace, PyTorch Lightning, weights & biases)* Background in neuroscience and/or oncology* Previous experience in pharma, biotech, or healthcare research* Experience integrating multimodal datasets (omics,
FULL_TIME