Mid-Level, Senior
StoneSoup's Bayesian pipeline (Predictor -> Hypothesiser -> DataAssociator -> Updater) tracks multiple targets using Kalman-family filters (KF, EKF, UKF, CKF) and probabilistic association (GNN, JPDA, EHM). Fixed process and measurement noise Q/R can cause filter divergence under model mismatch, while hand-crafted distance metrics can cause track coalescence and cubic association cost. This thesis replaces two pipeline stages with learned components: KalmanNET as the Updater, adapting Kalman gain K and noise covariances Q/R online via a GRU; and a transformer as the DataAssociator, producing association probabilities via attention instead of fixed gating.
Implement KalmanNET as a drop-in Updater, learning K, Q, and R from the innovation sequence
Implement a transformer-based DataAssociator using cross-attention between track and detection tokens
Build a StoneSoup simulation curriculum: linear to non-linear motion, low to high clutter, manoeuvring and crossing targets
Train both components on the curriculum with NEES/NLL/MSE loss for the filter and Hungarian-matched CE/GIoU loss for the associator
Evaluate against KF/EKF/UKF/CKF/IMM and GNN/JPDA/EHM2/TrackFormer using OSPA, MOTA, IDF1, NEES, and latency
Fine-tune and validate both components on Ericsson proprietary RAN measurement data
Working knowledge of Kalman filtering and Bayesian state estimation
Python proficiency, including PyTorch
Familiarity with recurrent networks (GRU/LSTM) and attention/transformers
Comfort with StoneSoup or similar tracking frameworks
Basic linear algebra and probability, including covariance, Cholesky decomposition, and Gaussian densities
Understanding of multi-object tracking metrics such as OSPA, MOTA, and IDF1
Experience with simulation-based training curricula
Git-based, reproducible experiment workflow
At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.
What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like. Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer. learn more.
Primary country and city: Sweden (SE) || Stockholm
Req ID: 791101
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