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The Wnt protein family plays a crucial role in cell development with each Wnt protein interacting uniquely with the Wls membrane protein through distinct binding residues. Dr. Capponi’s group (IBM Research) in collaboration with Prof Pogorelov group (UIUC) has previously uncovered the determinants of the interactions between four Wnt proteins and the Wls membrane protein by using atomistic simulations and machine learning techniques. The goal is to use machine learning approaches for time series to analyze the simulated trajectories and forecast differences in biological behavior.
As a research scientist intern you will work closely with an assigned mentor and will analyze the already collected data by building your own codes or using standard ML tools for analyzing time series from atomistic simulations. You will be studying the different contacts that the four Wnt proteins make with Wls highlighting the contact network and how this shapes the protein-protein interaction. You will contribute to designing the analysis of the simulations and making improvements to the current status of the project. You will also be expected to contribute to write a research report or paper on your internship project and present your work at internal research meetings and external workshops and/or conferences.
- Background in Computer Science Machine Learning Biochemistry or a related field.
- Proficiency in Python and technologies like TensorFlow PyTorch CUDA and Ray.
- Familiarity with high-performance computing environments and DevOps tools (GitHub).
- Demonstrating the ability to conduct and communicate impactful research and of working in teams.
- Experience in knowledge engineering machine learning and scientific software development.