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Postdoctoral Fellow Machine Learning Jobs in California

Moffitt

APPLIED POST DOC FELLOW

Long Beach, CA · On-site

$51K - $69K/yr

The Applied Postdoctoral Fellow will: Contribute to multi-modality translational studies that ... Having a collaborative spirit and being interested in learning the role of translational pathology ...

Arcadia Science

Platform Fellow

Emeryville, CA

$8.0K - $12K/mo

... machine learning tools, and evolutionary frameworks, powering research across Arcadia. This is an ideal opportunity for scientists in the final stages of their PhD or postdoc training who want to ...

Arcadia Science

Platform Fellow

Emeryville, CA · On-site

$8.0K - $12K/mo

... machine learning tools, and evolutionary frameworks, powering research across Arcadia. This is an ideal opportunity for scientists in the final stages of their PhD or postdoc training who want to ...

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

Velo3D

Machine Learning Engineer

Fremont, CA · On-site

$150K - $220K/yr

We are seeking a Machine Learning Engineer to join our team developing machine learning solutions ... Working closely with process engineers, software engineers, and fellow ML engineers, you will ...

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Postdoctoral Fellow Machine Learning information

What is a Postdoctoral Fellow in Machine Learning?

A Postdoctoral Fellow in Machine Learning is a researcher who has recently completed their PhD and is engaged in advanced research in the field of machine learning. This role typically involves conducting independent or collaborative research, publishing scientific papers, and sometimes mentoring students. Postdoctoral fellows often work at universities, research institutes, or industry labs, focusing on developing new algorithms, improving existing models, or applying machine learning techniques to specific problems. The position is usually temporary, lasting one to three years, and aims to prepare researchers for permanent academic or industry roles.

What is the difference between Postdoctoral Fellow Machine Learning vs Postdoctoral Research Scientist?

AspectPostdoctoral Fellow Machine LearningPostdoctoral Research Scientist
Required credentialsPhD in Computer Science, Data Science, or related fieldPhD in relevant field, often with specialized research experience
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, tech companies
Employer and industry usagePrimarily academia, government researchPrimarily industry, corporate research divisions
Common search and comparison intentUnderstanding academic research roles in machine learningExploring industry-focused research career paths

Postdoctoral Fellow Machine Learning roles typically focus on academic research, requiring a PhD and working in universities or research institutions. In contrast, Postdoctoral Research Scientist positions are often industry-based, emphasizing applied research within corporate R&D departments. Both roles involve advanced machine learning expertise but differ mainly in work environment and career trajectory.

What are the key skills and qualifications needed to thrive as a Postdoctoral Fellow in Machine Learning, and why are they important?

To thrive as a Postdoctoral Fellow in Machine Learning, you need a strong background in computer science, mathematics, and statistics, typically supported by a PhD and relevant research experience. Familiarity with programming languages such as Python, machine learning frameworks like TensorFlow or PyTorch, and experience in high-performance computing environments are commonly required. Strong analytical thinking, effective scientific communication, and collaboration skills help you contribute to research teams and disseminate findings. These skills and qualities are crucial for advancing research, developing innovative solutions, and building a successful academic or industry career in machine learning.

What are some common challenges faced by Postdoctoral Fellows in Machine Learning, and how can they be addressed?

Postdoctoral Fellows in Machine Learning often encounter challenges such as balancing independent research with collaborative projects, staying current with rapidly evolving technologies, and securing funding or publishing in top-tier journals. To address these, it's helpful to establish clear communication with mentors and collaborators, set aside dedicated time for reading recent literature, and actively seek feedback on research drafts. Building a professional network through conferences and seminars can also open opportunities for collaboration and career advancement.
What are popular job titles related to Postdoctoral Fellow Machine Learning jobs in California? For Postdoctoral Fellow Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Postdoctoral Fellow Machine Learning jobs in California look for? The top searched job categories for Postdoctoral Fellow Machine Learning jobs in California are:
What cities in California are hiring for Postdoctoral Fellow Machine Learning jobs? Cities in California with the most Postdoctoral Fellow Machine Learning job openings:
Postdoctoral Fellow Machine Learning jobs near you

Machine Learning Fellowship (6-12 months)

Volkswagen AG

Belmont, CA • On-site

Other

Posted 6 days ago

Job description

Brief Role Description

At the Innovation & Engineering Center California (IECC), we represent the Volkswagen Group in applied research and development. Located in the heart of Silicon Valley, we create bold new ideas for the Volkswagen, Audi, Bentley, Lamborghini, Bugatti and Porsche brands. We're a team of engineers, designers, scientists, and psychologists looking to develop innovations for future generations of cars, and to transfer technologies from many industries and research institutions into the automotive domain. Our mission is to drive change which means we are not only impacting one of the world's largest car makers, but also the lives of millions of people. Are you ready to join us?

*Six Month Minimum commitment  - Masters or PhD candidates only.
We are unable to consider International Students/OPT or CPT at this time.

Machine Learning Fellowship

Role Summary:

The perception and machine learning team is tasked to apply machine learning to the automotive industry. Applications include autonomous driving, manufacturing, material design, etc. The team develops state of the art AI solutions to solve complex and challenging problems by leveraging the latest techniques in machine learning on large data sets. At ICC, you will be involved in developing modern methods in the field of sensor data processing to enable safe and robust automated driving in any scenario. During this fellowship, you will be supporting a team of AI researchers and engineers to find suitable learning methods for robust perception.

Role Responsibilities

Role Responsibilities

  • Driving data pre-processing including checking labels, formatting, etc.
  • Support project team in building data pipelines for training deep neural networks.
  • Benchmarking and reporting of various neural network models performance.
  • Research into suitable new network architectures to for time series prediction, and anomalies detection.
  • Research into suitable new network architectures to improve the perception of the environment with a focus on optical sensors.
  • Research on supervised and unsupervised learning methods to estimate road participants' depth, motion, and velocity. 
  • Research on various neural networks for the interpretation of camera images (object detection, panoptic segmentation) and trajectory/motion planning.
  • Fusion of different neural networks to use shared resources to decrease memory and computation footprint.
Qualification requirements
  • Must be enrolled at a University/College or Graduation date must be within the last six months in a Masters or PhD program.
  • Must have a cumulative GPA of at least 3.0.
  • Strong Python programming skills.
  • Good experience using Linux.
  • Experience with deep learning frameworks such as TensorFlow and PyTorch.
  • Good knowledge of image processing and machine learning.
  • Independent work, initiative, motivation, ability to work in a team.
Competencies
Act as an owner
Be a pioneer
Excite customer
Handle complexity
Live integrity & compliance
Live passion
Perform together
Think ahead

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