Argonne’s Vehicle and Mobility Systems department (vms.taps.anl.gov) is seeking a pre-doctoral candidate to apply Machine Learning towards the goal of making vehicles more intelligent, and more energy efficient. In this position, the candidate will help build solutions to accelerate vehicle simulation and enable co-simulation with other tools including transportation systems tools.
As a Junior Machine Learning Engineer, you will work closely with our multidisciplinary team of researchers and engineers to design, develop, implement and deploy machine learning algorithms and models. Your primary responsibility will be to contribute to the ongoing research and development of machine learning solutions that improve the energy predictions of vehicles on various drive cycles and routes, as well as expand current model capabilities.
Collaborate with the VMS team to identify and define machine learning objectives.
Design and implement data processing pipelines to prepare data for machine learning tasks.
Develop, test, and refine current machine learning models.
Evaluate the performance of developed models and algorithms, iterating to improve their accuracy and efficiency.
Collaborate with software engineers to integrate machine learning models into vehicle simulation and transportation systems tools.
Stay current with the latest developments in machine learning, deep learning, and related fields to identify potential opportunities for application within the VMS department.
Contribute to the preparation of research papers, technical reports, and presentations to share findings and advancements with the scientific community and stakeholders.
MS in Computer Science, Data Science, Statistics, Applied Mathematics, or related field.
Experience with machine-learning/deep-learning: model selection, model optimization, model training and validation.
Programming experience in Python, and experience with Deep Learning frameworks (TensorFlow, PyTorch).
Experience in data analytics, including data management, signal processing, analysis, and visualization.
Experience with data processing and manipulation techniques, as well as experience working with large datasets.
A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
Experience or knowledge in vehicle simulation, transportation systems, or energy efficiency.
Long-Term (Fixed Term)
As 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.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.
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Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.