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Computational Protein Design - Postdoctoral Researcher

Company Description

Join us and make YOUR mark on the World!

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are committed to a diverse and equitable workforce with an inclusive culture that values and celebrates the diversity of our people, talents, ideas, experiences, and perspectives. This is important for continued success of the Laboratory’s mission.

Pay Range:

$112,800- $124,596

Please note that the pay range information is a general guideline only. Many factors are taken into consideration when setting starting pay including education, experience, the external labor market, and internal equity.

Job Description

We have multiple openings for Postdoctoral Researchers to join our interdisciplinary team of  Computational Bioengineers who will conduct research leading to our next-generation, machine learning-driven computational pipeline for protein design and optimizing protein-protein interactions as part of the Center for Predictive Bioresilience (CPB). CPB is an exciting and fast-paced engineering center combining predictive computational modeling, machine learning, and experimental biology to develop medical countermeasures.

You will work within a multi-disciplinary team with computational expertise in machine learning (ML), molecular simulation, optimization, and protein structure bioinformatics, and interface with our experimental team generating large datasets with novel high throughput assays aimed at informing predictive model development. You will leverage in-house computational tools and work to develop new machine-learning-based approaches and tools to design and optimize proteins (antibodies, immunogens, etc.) as therapeutics and vaccines. You will also work closely with an existing ML team to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools. You will be team-oriented and have experience working in a team environment to achieve common goals. While supporting applied research projects, you will be provided mentorship, practical training and skill development to develop depth and breadth in machine learning techniques as well as gain exposure to a variety of application areas. These positions are in the Computational Engineering Division (CED) within the Engineering Directorate, matrixed to the Center for Predictive Bioresilience.

You will

  • Work closely with project scientists and engineers and participate in the evaluation and implementation of  computational frameworks (e.g., large language model-based) optimized for protein design tasks.
  • Contribute to the development of analysis methodologies; analyze data; document research through presentations and peer-reviewed journal articles.
  • Support technical activities for new capability development and technical problem solving.
  • Document methods and ensure quality standards for project deliverables.
  • Publish research results in peer-reviewed scientific journals and present results at conferences, seminars, and meetings.
  • Travel as required to coordinate research with collaborators.
  • Perform other duties as assigned.

Qualifications

  • PhD in Machine Learning, Computational Biology, Statistics, Computer Science, Mathematics or a related field.
  • Fundamental knowledge and/or experience developing and applying algorithms in one or more of the following machine learning areas/tasks: protein structure machine learning, deep learning, unsupervised feature learning, zero- or few-shot learning, active learning, transformer-based language modeling, multimodal learning, ensemble methods.
  • Experience developing and implementing deep learning models and algorithms using modern software libraries such as PyTorch, TensorFlow, or similar as evidenced through publications or software releases.
  • Experience working with protein structures and domain knowledge in bioinformatics and protein structure modeling sufficient to communicate effectively with team members.
  • Experience working with a multidisciplinary team of scientists, engineers, and project managers to develop and apply these capabilities to inform engineering decisions.
  • Sufficient verbal and written communication skills to collaborate effectively in a team environment and present and explain technical information.
  • Ability to travel.

Qualifications We Desire

  • Ability to secure and maintain a U.S. DOE Q-level security clearance, which requires U.S. Citizenship.
  • Strong understanding of protein structure bioinformatics and/or protein structure prediction.
  • Experience with high-performance computing, GPU programming, parallel programming, cloud computing, and/or related methods including running numerical simulations of complex workflow.

Additional Information

Position Information

This is a Postdoctoral appointment with the possibility of extension to a maximum of three years, open to those who have been awarded a PhD at time of hire date.

Why Lawrence Livermore National Laboratory?

Security Clearance

This position requires either no security clearance, or a Department of Energy (DOE) L-level or Q-level clearance depending on the particular assignment.  

If you are selected and a security clearance is required, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing.  L and Q-level clearances require U.S. citizenship.  

If no security clearance is required, but your assignment is longer than 179 days cumulatively within a calendar year, you must go through the Personal Identity Verification process.  This process includes completing an online background investigation form and receiving approval of the background check.  (This process does not apply to foreign nationals.)

 

Pre-Employment Drug Test

External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor.

Wireless and Medical Devices

Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession.  This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices.  

If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas.  Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings.

How to identify fake job advertisements

Please be aware of recruitment scams where people or entities are misusing the name of Lawrence Livermore National Laboratory (LLNL) to post fake job advertisements. LLNL never extends an offer without a personal interview and will never charge a fee for joining our company. All current job openings are displayed on the Career Page under “Find Your Job” of our website. If you have encountered a job posting or have been approached with a job offer that you suspect may be fraudulent, we strongly recommend you do not respond.

To learn more about recruitment scams: https://www.llnl.gov/sites/www/files/2023-05/LLNL-Job-Fraud-Statement-Updated-4.26.23.pdf

Equal Employment Opportunity

We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.

We invite you to review the Equal Employment Opportunity posters which include EEO is the Law and Pay Transparency Nondiscrimination Provision.

Reasonable Accommodation

Our goal is to create an accessible and inclusive experience for all candidates applying and interviewing at the Laboratory.  If you need a reasonable accommodation during the application or the recruiting process, please use our online form to submit a request. 

California Privacy Notice

The California Consumer Privacy Act (CCPA) grants privacy rights to all California residents. The law also entitles job applicants, employees, and non-employee workers to be notified of what personal information LLNL collects and for what purpose. The Employee Privacy Notice can be accessed here.

Average salary estimate

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$112800K
$124596K

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What You Should Know About Computational Protein Design - Postdoctoral Researcher, LLNL

Are you ready to make a significant impact in the field of protein design? Join the dynamic team at Lawrence Livermore National Laboratory (LLNL) as a Computational Protein Design - Postdoctoral Researcher! This exciting role offers you the unique opportunity to collaborate with some of the brightest minds in the industry, focusing on revolutionary machine learning-driven computational pipelines for protein design and optimization. Located in beautiful Livermore, CA, you'll be part of a diverse and inclusive culture where everyone’s ideas and talents are valued. At LLNL, we combine computational modeling, machine learning, and experimental biology to tackle pressing problems and develop medical countermeasures. In this position, you'll work with a multi-disciplinary team, applying your computational expertise in areas such as machine learning, molecular simulation, and protein structure bioinformatics. Collaborating closely with experimental teams, you will design and optimize proteins for therapeutics and vaccines using advanced methodologies and state-of-the-art tools. Your contributions will be pivotal in shaping the future of predictive modeling and developing exceptional protein design models. We’re looking for someone with a PhD in a relevant field and a strong foundation in machine learning techniques, deep learning models, and bioinformatics. Enjoy a vibrant work environment at LLNL, with competitive pay and flexible benefits, while advancing your career in a truly innovative lab! This is your chance to make a difference!

Frequently Asked Questions (FAQs) for Computational Protein Design - Postdoctoral Researcher Role at LLNL
What are the main responsibilities of the Computational Protein Design - Postdoctoral Researcher at Lawrence Livermore National Laboratory?

As a Computational Protein Design - Postdoctoral Researcher at Lawrence Livermore National Laboratory (LLNL), your primary responsibilities include developing and optimizing machine learning-driven computational pipelines for protein design. You will work collaboratively within a multi-disciplinary team to analyze data, implement computational frameworks, and contribute to research that informs engineering decisions in the field of predictive bioscience.

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What qualifications are required for the Computational Protein Design - Postdoctoral Researcher position at LLNL?

To qualify for the Computational Protein Design - Postdoctoral Researcher role at Lawrence Livermore National Laboratory, you must possess a PhD in Machine Learning, Computational Biology, or a related field. Additionally, experience in developing algorithms for machine learning tasks, particularly in protein structure modeling, as well as familiarity with deep learning frameworks like TensorFlow or PyTorch, is essential.

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Is previous experience with protein structures necessary for the Computational Protein Design - Postdoctoral Researcher role at LLNL?

Yes, previous experience with protein structures is crucial for the Computational Protein Design - Postdoctoral Researcher position at Lawrence Livermore National Laboratory. A strong understanding of protein structure bioinformatics will enable you to communicate effectively with fellow team members and contribute to the development of protein design methodologies.

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What kind of work environment can I expect as a Computational Protein Design - Postdoctoral Researcher at LLNL?

At Lawrence Livermore National Laboratory, the work environment is collaborative and inclusive. As a Computational Protein Design - Postdoctoral Researcher, you will be part of an interdisciplinary team where diversity of thought is encouraged. The laboratory promotes flexible work schedules and values impactful contributions to research to support the United States’ security and scientific advancement.

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What opportunities for professional development are available for a Computational Protein Design - Postdoctoral Researcher at LLNL?

Lawrence Livermore National Laboratory offers several opportunities for professional development for Computational Protein Design - Postdoctoral Researchers. You will receive mentorship, practical training in machine learning techniques, and exposure to various application areas. Your research will also be published in peer-reviewed journals, and you will have the chance to present your findings at conferences, which is invaluable for career growth.

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Common Interview Questions for Computational Protein Design - Postdoctoral Researcher
What machine learning techniques are you most familiar with in the context of protein design?

When answering this question, focus on specific machine learning methods you've used, such as deep learning, unsupervised learning, or ensemble methods, and relate them directly to protein design projects. Highlight your experience with relevant software libraries like TensorFlow and PyTorch, and discuss how you've applied these techniques to real-world problems in your previous research.

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Can you describe a project where you applied machine learning to biological data?

Provide a detailed description of a relevant project, including the objectives, methods, and outcomes. Emphasize your role in the project, the algorithms you employed, and how your findings contributed to the field. Use this opportunity to showcase your problem-solving skills and ability to work with interdisciplinary teams.

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How do you ensure the quality of your computational models and results?

Discuss the methodologies you use to validate your computational models, including testing, cross-validation, and peer review. Share your approach to documenting procedures and results to maintain high standards in your research work. Emphasize the importance of reproducibility and transparency in computational science.

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Describe your experience collaborating with experimental biologists or chemists.

In your response, highlight your ability to work cross-functionally with teams from different disciplines. Discuss specific instances where you've successfully communicated complex computational findings to experimental colleagues and how this collaboration led to meaningful results, such as new experimental designs or improved protein designs.

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What challenges have you faced in protein design research, and how did you overcome them?

Talk about specific challenges related to data integration, algorithm implementation, or modeling accuracy in your past projects. Outline your problem-solving strategies, such as seeking collaboration, refining your approach, or utilizing additional data sources, and highlight any metrics or outcomes that demonstrate your success in overcoming these challenges.

Join Rise to see the full answer
What experience do you have with high-performance computing and parallel programming?

Share details about your experience working on high-performance computing platforms, including how you've leveraged parallel programming techniques to enhance computational efficiency in your research work. Discuss specific software tools or frameworks you've used and how they contributed to the success of your projects.

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How do you stay updated with the latest advancements in computational biology and protein design?

Explain your strategies for staying informed, such as following relevant journals, attending conferences, participating in workshops, and engaging with the broader scientific community. Emphasize your enthusiasm for continuous learning and how you integrate new knowledge into your work at LLNL.

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What specific machine learning models would you use for optimizing protein-protein interactions?

Provide a brief overview of the models you're familiar with, such as supervised learning methods or reinforcement learning, and explain your reasoning for choosing them based on the problem context. Discuss any relevant experiments you've conducted to validate model performance in optimizing protein interactions.

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Describe your experience with data analysis and visualization tools.

Discuss various software and tools you've used for data analysis, such as Python libraries, R, or specialized bioinformatics tools. Highlight how you've employed visualization techniques to interpret complex datasets and communicate results effectively to both technical and non-technical stakeholders.

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How would you approach a project that requires collaboration among multiple scientific disciplines?

Articulate your approach for facilitating collaboration, including establishing clear communication channels, understanding diverse perspectives, and leveraging each team member's expertise. Share examples from your experience where you successfully navigated interdisciplinary projects, emphasizing the importance of teamwork in achieving research goals.

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Established in 1952 and headquartered in Livermore, California, The Lawrence Livermore National Laboratory (LLNL) is a scientific research laboratory founded by the University of California. The laboratory is primarily funded by the United States ...

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December 24, 2024

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