-
and adaptive token pruning; Distributed and collaborative inference strategies; Mixture-of-Experts (MoE) architectures for scalable inference; Resource-aware and latency-constrained inference
-
architectures for remote sensing super-resolution, including training strategies, loss functions, and generalization assessment. Applying and assessing explainable AI methods (e.g. Shapley values) to interpret
-
of neural network architectures (as a plus: PINNs, neural operators, transformers/LLM) and NN training. Strong Python programming skills (as a plus: C++ or Julia) and knowledge of scientific computing
-
Collaborating across domains, including civil/architectural engineering and energy management This position is deeply interdisciplinary, and will be carried out in close collaboration with The successful
-
will be in developing new techniques for testing and verifying modern highly concurrent systems, such as weak-memory architectures and highly-distributed databases. The position is also open, to some
-
experience in system architecture design The following qualifications would be considered strong advantages: Experience with edge AI frameworks (e.g., TensorFlow Lite) Familiarity with mission control systems