Doktorand i maskininlärning
Core
Developing mathematically grounded methods for uncertainty quantification in deep learning, specifically focusing on large language models for healthcare applications.
Role type
PhD researcher (Doctoral candidate)
Builds
Probabilistic time-to-event models integrating uncertainty from medical text predictions.
Domain
Data-driven life science / Computational biology / Healthcare
Deliverable
production ML models
Required skills
Applied mathematics, Applied statistics, Technical physics, Linear algebra, Probability theory, Analysis, Programming
Preferred skills
Bayesian statistics, Mathematical modeling, Probabilistic machine learning, Deep learning, Large language models
Responsibilities
Conduct independent research on uncertainty quantification in deep learning; Integrate uncertainty into probabilistic models; Apply methods to prostate cancer data and unstructured medical text; Teach and perform administrative duties (max 20%).
Seniority
PhD Candidate