Doctoral Researcher in Probabilistic Machine Learning
Tasks
- Develop biology informed gaussian processes
- Develop interpretable mechanistic odes
- Handle partial observability
- Incorporate uncertain prior knowledge
- Infer from heterogeneous trajectories
- Integrate mechanistic odes with gaussian process terms
- Learn symbolic structure of odes
- Model population and subject specific dynamics
- Model temporal single cell data
- Perform bayesian inference for dynamical systems
- Quantify uncertainty in dynamical models
Perks/Benefits
Skills/Tech-stack
Applied Mathematics | Bayesian Inference | Bayesian Machine Learning | Computer Science | Differential Equations | Dynamical systems | Gaussian Processes | Machine Learning | Monte Carlo | Monte Carlo Methods | Ordinary differential equations | Probabilistic Machine Learning | PyTorch | Python | R | Stan | Statistics | TensorFlow | Uncertainty Quantification | Variational Inference
Education
Roles
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