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
Job Description
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.
Position Requirements
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.
Preferred Qualifications:
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 Family
Postdoctoral FamilyJob Profile
Postdoctoral AppointeeWorker Type
Long-Term (Fixed Term)Time Type
Full timeAs an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
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Argonne is an equal opportunity employer, and we value diversity in our workforce. As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne prohibits discrimination or harassment based on an individual's age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
