Post-doctoral Appointee – AI for Intelligent Vehicles
- Argonne National Laboratory
- Location: Lemont, IL
- Job Number: 7122716 (Ref #: 414820)
- Posting Date: Feb 1, 2023
- Application Deadline: Open Until Filled
Argonne’s Vehicle and Mobility Systems department (vms.taps.anl.gov) is seeking a post-doctoral candidate to apply machine-learning, deep-learning, reinforcement learning and other AI techniques towards the goal of making vehicles more intelligent, and more energy-efficient. In this position, the candidate will help solve many challenges, including:
Data-driven driver model. Future vehicle speed prediction, using as input route information from navigation system, and prior driving history. Applications include EV range prediction, horizon generation for driving/powertrain control optimization, eco-routing, drive cycle generation, etc. The candidate will train the model on very large databases of real-world driving data, and special attention will be paid to demonstrating the validity of the model.
AI for “eco” automated driving controls. Applying AI to longitudinal speed control with energy-efficiency as one of the objectives. The input to the AI will be information from sensors, V2V communications and digital maps. Training will involve running very-large scale simulations of automated driving vehicles using HPC. After demonstration of robustness and performance in simulation, the newly developed controls will then be deployed and demonstrated in experimental automated vehicles.
The candidate will develop AI models, run large-scale tests to demonstrate proper functionality, integrate the models with other models and software, document and deploy internally and publish papers on the topic.
The candidate will join a large multi-disciplinary team that collaborates with partners across the lab, with industry and academia, and with access to world-class HPC and experimental facilities.
PhD in Computer Science, Data Science, Statistics, Applied Mathematics, or related field.
Extensive experience with machine-learning/deep-learning: model selection, model optimization, model training and validation.
Knowledge in statistical modeling.
Experience in data analytics, including data management, signal processing, analysis, and visualization.
Programming experience in Python, and experience with ML frameworks (Tensorflow, PyTorch).
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Experience in reinforcement-learning.
Experience with probabilistic deep learning and generative models.
Experience with high-frequency (1 Hz or more) spatio-temporal data.
Experience in software development, incl. source control, documentation, testing, release generation.
Experience in optimization.
Experience in automatic control.
Experience in vehicle, driving, traffic or transportation modeling.
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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