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The successful candidate will support the expanding research activities at the Department of Cancer Research (DoCR) and the Department of Medical Informatics (DMI) with a particular emphasis on high-throughput, reproducible, service-oriented bioinformatics for cancer research programs, with a strong focus on sequences, multi-omics data, and single-cell datasets. In collaboration with DoCR researchers, she/he will engage in cutting-edge research and applied projects using machine learning, deep learning, and omics data analysis
Key Accountabilities
- Support research groups in the planning and implementation of AI-driven analyses of molecular data, focusing on single-cell and bulk multi-omics and long-read sequencing.
- Develop, train, and validate machine learning and deep learning models for multi-omics integration to learn the shared and modality-specific representations across multiple data types.
- Use pre-trained foundation models developed for biological sequences or single-cell and bulk omics. Fine-tune large models for specific tasks, e.g., variant effect prediction, cell state representation, annotation, perturbation prediction, and classification.
- Generative AI for omics: develop and apply probabilistic generative models (e.g., VAEs and related approaches) for modeling omics data.
- Implement and maintain scalable pipelines for protein structure prediction, including state-of-the-art tools such as AlphaFold. Perform digital drug screening with the existing tools.
- Work closely with biologists, clinicians, biostatisticians, and computer scientists, support their AI/ML needs, and translate research into real-world applications.
- Contribute to the training of LIH staff members and PhD students.
- MSc/PhD degree in computer science, biomedical engineering, data science or related.
- At least 2 years of experience in a similar position.
- Expertise in AI/ML and deep learning methods, with a rigorous approach to model evaluation and validation. Experience in handling and understanding omics data.
- Strong programming skills in Python. Knowledge of R is considered an asset.
- Proficiency with modern deep learning frameworks, such as PyTorch and TensorFlow.
- Experience working in high-performance computing (HPC) environments: use of cluster computing, job schedulers, and containerization.
- Excellent problem-solving, organizational, and time-management skills.
- Strong communication skills and ability to work effectively in a team and in close collaboration with biologists and clinicians.
- Self-motivated, team spirit, and service-oriented mindset.
- Language skills: fluent command of English. Knowledge of French and/or German is considered an advantage.
Key Skills
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