Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
related research directions in the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural
-
, electrical engineering and computer science to design highly efficient and sensitive imaging and inference approaches to help guide diagnosis and treatment in cardiovascular patients. Project background Our
-
in teaching. Profile Doctoral degree in biology, computational biology, applied mathematics, physics or equivalent Proven track record in infectious disease modelling, quantitative biology or related
-
the admission requirements for a PhD at ETH Zurich Experience in machine learning, optimization, or AI-driven decision-making Preferably with knowledge of Bayesian optimization or Gaussian processes
-
Prof. Livia Schubiger. The candidate will work with the IRDS group on projects that leverage NLP, causal inference, and machine learning to explore norms related to gender-based and other forms
-
) for engineering systems. Our research covers surrogate modeling, reliability analysis, sensitivity analysis, optimization under uncertainty, and Bayesian calibration. We are known for developing the UQLab software
-
recovery trajectories and injury patterns. Integrate personalized physiological measurements into a recovery prediction model, while adapting Bayesian Neural Networks for SCI data and analyzing the impact on
-
: Experience in machine learning, optimization, or AI-driven decision-making Preferably knowledge of Bayesian optimization or Gaussian processes Programming experience (Python, MATLAB, or similar) Soft Skills
-
, functional genomics, protein engineering, and targeted protein degradation. Project background High-throughput perturbation technologies rely on perturbing DNA or RNA to infer the function of proteins. Methods
-
We look forward to receiving your application with the following documents as a single PDF: A cover letter indicating which track you are applying for (RL/Optimization, LLM/Knowledge or both) CV