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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.
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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
Computational Postdoctoral Fellow (Quantitative Modeling Group)

Computational Postdoctoral Fellow (Quantitative Modeling Group)

Lawrence Berkeley National Laboratory

Berkeley, CA • On-site

$60K - $81K/yr

Other

Medical, PTO

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Berkeley Lab's (LBNL) Biological Systems and Engineering (BSE) Division has an opening for a Computational Postdoctoral Fellow to join the Quantitative Modeling Group led by Hector Garcia Martin!

In this exciting role, you will leverage Artificial Intelligence, data science, mechanistic models, robotics, and synthetic biology to enable quantitative predictions of biological systems and support the development of self-driving laboratories. You will contribute to the design of cells and other biological systems to a specification, with the final goal of enabling the full potential of biomanufacturing to create enhanced-performance products, diminish supply chain disruptions, produce environmental benefits, address national security needs, and create new jobs and industries.

You will have the opportunity to work collaboratively to integrate microbial phenotypic data (e.g., fluxomics, transcriptomics, proteomics, metabolomics) into quantitative computational models capable of predicting and explaining the outcomes of bioengineering interventions. You will work closely with an interdisciplinary team of bench scientists, automation engineers, and software engineers in devising methods for high-throughput data collection and analysis for feedback into experimental design. This work will support initiatives within the Agile BioFoundry, the Joint BioEnergy Institute, and/or other programs.

This position has an anticipated start date of September 1, 2026.

We're here for the same mission, to bring science solutions to the world. Join our team and YOU will play a supporting role in our goal to address global challenges! Have a high level of impact and work for an organization associated with 17 Nobel Prizes!

Why join Berkeley Lab?

We invest in our employees by offering a total rewards package you can count on:

  • Exceptional health benefits.
  • Generous paid time off, sick time off, and holidays.
  • A culture where you'll belong - we are invested in our teams!

What You Will Do:

  • Integrate and analyze complex biological datasets.
  • Develop quantitatively predictive models of biological systems.
  • Integrate multi-omics data into quantitative computational models.
  • Apply Monte Carlo sampling approaches to quantify uncertainty.
  • Utilize machine-learning and data-mining approaches to recommend bioengineering interventions.
  • Develop new machine-learning algorithms.
  • Integrate machine learning techniques with mechanistic modeling approaches.
  • Develop, optimize, and maintain code and algorithms supporting predictive models.
  • Combine computational algorithms with laboratory automation to enable self-driving laboratories and automate the scientific process.
  • Collaborate closely with experimental and automation scientists to guide experimental design and maximize the value of available data to its full potential.
  • Partner with software engineers to develop and maintain code following best practices.
  • Troubleshoot technical and research problems that may affect the achievement of research objectives and deadlines.
  • Prepare research results for publication and present results at scientific conferences, seminars, and internal meetings.
  • Contribute to the preparation and development of grant proposals and related supporting materials.

What is Required:

  • A recent Ph.D. (within the last 1-2 years) in Physics, Applied Mathematics, Computer Science, Electrical Engineering, Chemical Engineering, Mechanical Engineering, Systems Biology, Bioengineering, Computational Biology, Bioinformatics, or a closely related discipline.
  • Demonstrated experience programming in Python or other major programming languages.
  • Proven experience working in Linux environments, including file systems, shell scripting, and hardware/software monitoring.
  • Strong mathematical background as evidenced by relevant coursework and/or work experience.
  • Strong organizational skills including experience maintaining detailed and accurate records of results and analyzed data.
  • Excellent verbal and presentation skills including experience preparing research reports, manuscripts, and scientific publications for group meetings, conferences, and scientific journals.
  • Demonstrated interpersonal communication skills including experience conducting independent, data-driven research and collaborating with an interdisciplinary research team.

Desired Skills/Knowledge:

  • Experience conducting experimental laboratory work.
  • Experience with metabolic flux analysis.
  • Knowledge of microbiology and microbial metabolism.
  • Strong interest in biology and metabolism.

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