Graduate Research Intern - Machine Learning for Protein Structure Prediction

As a Graduate Research Intern specializing in Machine Learning for Protein Structure Prediction at Iambic Therapeutics, you'll be responsible for developing and training deep learning models aimed at predicting protein structures. You'll analyze large-scale protein structural data to uncover patterns, work with molecular dynamics simulations, validate model predictions, and assist in implementing new model architectures. Collaborating closely with your team, you will also present your findings to contribute to our model evaluation framework, particularly focusing on conformational accuracy.

To qualify for the Graduate Research Intern position at Iambic Therapeutics, candidates should be currently enrolled in a PhD program in fields such as Computational Chemistry, Computational Biophysics, Computer Science, or Computational Biology. Candidates should possess proficiency in Python, have experience with enhanced sampling and classical molecular dynamics simulations, and be familiar with deep learning frameworks, preferably PyTorch. A solid understanding of protein structure and dynamics is also essential, alongside a keen interest in scientific machine learning applications.

During the Graduate Research Internship at Iambic Therapeutics, you'll gain invaluable experience working with state-of-the-art protein structure prediction models. You'll learn how to integrate physical principles into machine learning approaches and gain hands-on experience with enhanced sampling methods. Moreover, this internship provides insight into real-world applications in drug discovery, allowing you to collaborate with leading experts in machine learning and computational chemistry.

Yes! The Graduate Research Internship at Iambic Therapeutics is a paid position. In addition to competitive pay, interns also enjoy benefits that include company-paid healthcare, flexible spending accounts, and 401K matching. This ensures that our interns are well-supported during their time with us in San Diego.

The duration of the Graduate Research Internship at Iambic Therapeutics is flexible, ranging from 3 to 9 months, depending on the intern's academic schedule. This flexibility allows you to balance your academic commitments while gaining valuable research experience in a thriving environment.

When answering this question, focus on specific projects or coursework that involved Python. Explain how you utilized Python for data analysis, model development, or any machine learning frameworks. Highlight examples that directly relate to protein structure prediction or molecular dynamics simulations.

Respond by discussing any specific molecular dynamics software you are familiar with, and describe the scenarios where you applied these techniques. Emphasize your understanding of enhanced sampling methods and how they can improve model predictions in protein structure research.

Choose a relevant example where you encountered difficulties related to protein structure prediction or model training. Discuss the strategies you implemented to address the issue, including any collaborative efforts or innovative solutions you developed.

Share your genuine passion for the intersection of machine learning and biological applications. Discuss specific examples of how you see machine learning revolutionizing the field of structural biology and drug discovery, as well as your personal motivation for joining Iambic Therapeutics.

Acknowledge the importance of constructive criticism and collaboration in research. Provide examples of past experiences where you received feedback and successfully adapted your methodologies or project focus as a result.

Be prepared to discuss specific deep learning models you've worked on, including any relevant architectures that you adapted or created. Detail any successes or lessons learned from your experimentation to show your depth of knowledge.

Describe your methodology for analyzing large datasets, emphasizing computational tools you've utilized and specific analytical techniques. Include your approach to identifying patterns or anomalies in protein conformational changes.

Reflect on past team projects, emphasizing your communication skills and the importance of collaboration. Illustrate your role in the team, how you contributed to discussions, and how collective efforts led to successful outcomes.

Discuss your understanding of model validation techniques, like cross-validation, and emphasize the importance of performance metrics relevant to protein structure predictions, such as conformational accuracy, precision, and recall.

Share your insights on emerging trends in computational biology, particularly in machine learning. Discuss areas such as AI-driven drug discovery, integrating biological data with computational methods, and advancements in predictive modeling.