MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Principal Investigators: Professor Alan Edelman & Dr. Chris Rackauckas
We are seeking a highly motivated postdoctoral researcher to join a collaborative research project between MIT and GSK, focusing on the integration of Scientific Machine Learning (SciML) with Quantitative Systems Pharmacology (QSP) models. This exciting opportunity combines cutting-edge computational methods with pharmaceutical research to develop next-generation high-fidelity predictive models.
The successful candidate will work on developing and training advanced emulator models using stiff ODE QSP systems, with applications to HIV, HBV, and/or other disease models. The research will also explore automated discovery of missing elements of models using universal differential equations. The project aims to assess whether combining QSP model emulation with available data can produce superior predictive accuracy compared to traditional approaches.
Develop and train state-of-the-art emulator models using stiff ODE QSP describing disease models (HIV, HBV, and/or others)
Build predictive ML models using preclinical and clinical training data
Create SciML models combining QSP/QST frameworks with clinical datasets
Integrate novel datasets (e.g., Omics data) with traditional QSP approaches
Develop open source Julia software packages for the research community
Collaborate closely with GSK researchers through regular meetings
Publish research findings in high-impact journals and present at conferences
PhD in Computer Science, Applied Mathematics, Computational Biology, Bioengineering, or related field
Strong programming experience in Julia (preferred) or willingness to learn
Demonstrated major contributions to open source libraries on the topic, in particular Julia open source libraries and those from the SciML organization
Experience with differential equations, particularly stiff ODEs
Background in machine learning and scientific computing
Knowledge of pharmacometrics, systems biology, or related fields
Strong publication record and communication skills
Experience with Scientific Machine Learning (SciML) frameworks
Background in pharmacokinetic/pharmacodynamic modeling
Familiarity with quantitative systems pharmacology
Experience with neural ODEs or physics-informed neural networks
Knowledge of pharmaceutical drug development processes
Duration: 2 years with possibility of extension
Start Date: As soon as possible
Location: MIT CSAIL, Cambridge, MA
Funding: Fully funded position with competitive salary and benefits
Travel: Opportunities for conference travel and professional development
This position offers a unique opportunity to work at the intersection of computational science and pharmaceutical research. You will be part of the vibrant Julia Lab at MIT while collaborating with industry partners at GSK. You will be integrated with the Kendall Square pharmaceutical community, which hosts many of the largest pharmaceutical firms in the world. The research will contribute to open source software development and aims to produce high-impact publications.
Regular interactions with GSK researchers will provide valuable industry perspective and potential career networking opportunities. The successful candidate will have access to cutting-edge computational resources and will be encouraged to participate in the broader SciML and Julia communities.
Please submit the following materials:
Cover letter describing research interests and relevant experience
Current CV including publication list
Contact information for three professional references
Brief research statement (1-2 pages) outlining your vision for this project
Application Deadline: Open until filled Contact: Dr. Chris Rackauckas (crackauc@mit.edu)
If you are interested in any of these projects and are a current MIT student looking for a UROP or MEng please reach out to the mentor listed next to project.
A large list of projects in scientific machine learning can be found here. Take that list as a set of ideas from which larger projects can be chosen.
Mentor: Julian Samaroo
Implement support for various ROCm libraries: rocSOLVER, rocSPARSE, MIOpen, etc.
Build ROCm libraries as JLLs
Explore integration with ROCm debugging and profiling tooling
Enzyme.jl is the Julia frontend to the Enzyme automatic-differentiation engine.
Improved JIT compilation for Enzyme
Compile on Demand / Parallel JIT
Caching of Enzyme AD results
Caching of inference results for reducing inital latency
Improvements to Julia integration with native debuggers and profilers
Better native debug-information (DWARF)
Pretty-printers for GDB
Debug-information on demand
Exploring profile-guided optimization
Feasibility study on reducing the size of an a-HfO2 dataset using a parallel method based on HDBSCAN and ACE. A parallel Julia implementation of a state of the art method will be required as well as the proposal of an improved version aligned to CESMIX objectives. Description here. Contact: Emmanuel Lujan (eljn AT mit DOT edu)
One of the main challenges of atomistic simulations is the acceleration of force calculations. Machine learning potentials promise the accuracy of first-principles methods at a lower computational cost. Simplifying the creation of these potentials (composed of data, descriptors and learning methods) enables systematizing the search for those combinations that exceed the accuracy and performance of the state of the art. This requires the development of new software abstractions and parallel tools. A more detailed description of the project can be found here. Contact: Emmanuel Lujan (eljn AT mit DOT edu).
Mentors: Rabab Alomairy and Evelyne Ringoot
E.g. matrix decompositions algorithms for GPUS, migration of BLAS routines (C) to Julia language and other numerical linear algebra. Interested students who have taken 18.06 or equivalent, and have experience in either julia or C/C++, (great if experience with slurm/supercomputers), please reach out to Evelyne Ringoot and Rabab Alomairy with a resume and github profile link.
In 1990 Trefethen and Schreiber produced an influential paper on the average case stability of Gaussian elimination with partial and complete pivoting: paper link. In Eq. (6.2) and Figure 6.2 they suggest (with a clear caveat) that the growth is n^(2/3) and n^(1/2). Some years later I histogrammed some values of n maybe 1000, 2000, and 4000 (I'd have to dig it up – buried in my files), and perhaps I histogrammed g/n^(1/2) or g/n^(2/3) and found one lined up nice and the other did not. See what you can find.
Over the years people have said that an LAPACK rewritten in Julia could have more interesting properties, and also have a smaller codebase if done carefully. Find something in Generic Linear Algebra.jl that is not there currently and add to it, and check that it runs at least as fast as original LAPACK, but perhaps works on quaternions, or funny number fields, or matrices of matrices etc., and that you can run autodiff on these constructs.