Doktorand i AI och maskininlärning för cancerforskning
Core
Developing AI models to predict how glioblastoma cell behavior changes in response to genetic and pharmacological interventions, aiming to reprogram tumor states for better treatment sensitivity.
Role type
PhD researcher in AI and machine learning for cancer research
Builds
Multimodal AI models integrating large-scale intervention experiments and real-time cell tracking image data
Domain
Neuro-oncology, computational biology, and machine learning
Deliverable
production ML models
Required skills
Linear algebra, probability theory, statistical inference, Python, R, large-scale data analysis, image analysis, genomic data analysis
Preferred skills
Single-cell RNA-seq analysis, CRISPR screening, deep learning, graph neural networks, dynamic system modeling, system control, method development
Technologies
Hidden Markov Models (HMM), Perturb-seq, CRISPR-reporter tools
Responsibilities
Design and execute large-scale intervention experiments to map genetic and pharmacological effects on tumor cell plasticity; analyze image-based tracking data of individual tumor cells in real-time; develop and refine AI models to predict treatment outcomes based on combined data sources; implement cyclical experimentation where model predictions guide the next experiment and results are fed back into the model.
Seniority
PhD candidate (early career researcher)