Analyze RNA-seq datasets, including preprocessing, QC, normalization, and differential expression analysis
Develop and maintain bioinformatics pipelines for transcriptomic data
Apply statistical and machine learning methods to identify diagnostic or predictive biomarkers
Build and evaluate predictive models, including deep learning approaches
Explore applications of large language models (LLMs) or multimodal AI in biological data analysis
Collaborate with cross-functional teams to interpret results and guide experimental design
Communicate findings through reports, visualizations, and presentations

PhD candidate or PhD degree in Bioinformatics, Computational Biology, Biology, or a related field
Hands-on experience with RNA-seq data analysis
Strong programming skills in R and/or Python
Familiarity with Linux/Unix environments and proficiency in bash scripting
Formal training in statistics and machine learning
Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
Experience developing or applying deep learning models
Familiarity with large language models (LLMs) or generative AI applications in biology
Experience with NGS pipelines and tools (e.g., STAR, Salmon, nf-core, etc.)
Experience working in high-performance computing (HPC) or cloud environments
Strong data visualization and communication skills

Part-Time Bioinformatics Scientist – Contract/Consultant

Key skills