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Graduate Research Opportunities
We are looking for highly motivated and talented graduate students with interests in computational materials science and/or biophysical simulation for research opportunities in:

  • Machine learning inverse design of self-assembling colloids and peptides
  • Computational vaccine design for hepatitis C virus and flu
  • QSAR design of antimicrobial cell penetrating peptides (collaboration w/ Gerard Wong, UCLA)

No prior experience in computer programming or molecular simulation is necessary, but a strong background in mathematics and physical sciences is required.

Interested students should contact Prof. Ferguson directly at
alf@illinois.edu for more information.
Undergraduate Research Opportunities
We are looking for highly motivated and talented UIUC undergraduates from all departments for the following research opportunities available for academic year 2016-17. Interested undergraduate students from all departments should contact Prof. Ferguson directly at alf@illinois.edu enclosing a current (unofficial) transcript and CV.

1. Computational design of hepatitis C vaccines using GPU-accelerated Bayesian inference

The goal of our research is to develop a computational platform to design hepatitis C virus (HCV) vaccines. Our method rests on the idea of a fitness landscape describing the viability of the virus as a function its amino acid sequence, which allows us to rationally design vaccines to target and eliminate the most dangerous viral strains. We have developed a computational approach to determine the HCV fitness landscape by data mining clinical sequence databases using a Bayesian inference algorithm. We are currently interested in speeding up our inference code through GPU parallelism to accelerate inference pipeline and enable the design of vaccines against larger HCV proteins, and running dynamic simulations of viral evolution under host immune pressure over the empirical fitness landscapes.

This undergraduate research position requires a strong background and expertise in C++ and CUDA programming. Knowledge of other acceleration methods or parallelization standards (e.g., OpenACC, MPI, OpenMP) are welcome. No knowledge of biology or immunology is required.

2. Computational study of asphaltene aggregation using molecular dynamics simulation

Asphaltenes constitute a heavy aromatic crude oil fraction with a propensity to aggregate and precipitate out of solution during petroleum processing. Downtime and inefficiencies caused by asphaltene fouling costs the petroleum industry billions of dollars per year. Aggregation is thought to proceed hierarchically according to the Yen-Mullins model, but the molecular mechanisms underlying mesoscopic aggregation remain poorly understood. In this undergraduate research project, the student will work closely with the faculty member and graduate researcher to perform mesoscale molecular dynamics simulations of asphaltene aggregation on GPU hardware, and perform nonlinear machine learning to determine the molecular mechanisms of aggregation as a function of molecular architecture and environmental conditions.

Qualified applicants for this undergraduate research position will possess a strong background in physical sciences (math, physics, chemistry), and competency in C++ and Matlab programming.

3. Mesoscale molecular simulations of self-assembling organic electronic nanowires

Certain biological molecules containing aromatic rings can self-assemble into 1D nanowires possessing overlapping π orbitals, which leads to electron delocalization and interesting conductive, optical, and photophysical properties. These organic electronic nanoaggregates have a variety of applications, from organic photovoltaic cells to the LEDs found in the new Apple watch. This project will use mesoscopic molecular dynamics simulations and machine learning to understand the self-assembly of a family of optoelectronic peptides to determine the molecular-level effects of changing chemistry on aggregate structure and properties.

This undergraduate research position requires a strong background in physical sciences, and familiarity with Linux, bash shell, and Matlab. No prior knowledge of biology or chemistry is required.

4. Molecular design of self-assembling antimicrobial nanostructures

Antimicrobial resistance poses a serious public health issue, affecting millions and costing over $55 billion in annual health care costs in the United States alone. We are interested in designing alternative treatments to traditional drug therapy using self-assembling nanostructures formed from short amphiphilic peptides that have been shown to preferentially bind to bacterial cell walls and induce cell lysis and death. This project will use mesoscale molecular simulation of these short peptides alongside machine learning to understand the effects of peptide sequence on aggregate structure, thermodynamics, and kinetics.

Qualified applicants for this undergraduate research position will possess a strong background in physical sciences and familiarity with Linux, Python, and Matlab. C/C++ experience is welcome. No prior knowledge of biology or chemistry is required.