Chemical Theory Center
Meet CTC is a series of interviews conducted with members of the Chemical Theory Center. Each profile will feature the scientist’s research and its real world applications, along with their life outside of academia.
Daniel Graham is a fourth-year Ph.D. student in the Goodpaster group. Daniel is from Sacramento, California, and he received his undergraduate degrees in chemistry and computer science at Centre College in Danville, Kentucky. Daniel’s current project focuses on developing density functional theory (DFT) embedding using the Huzinaga level shift projection operator. He has developed and benchmarked the method for closed-shell systems, and is now working on the open-shell version of the code. After completing his Ph.D., Daniel hopes to teach at a liberal arts college. He likes mentoring students and gets excited when he sees someone understand something they didn't know before.
Carlo Alberto Gaggioli is a postdoctoral researcher in the Gagliardi group. A Perugia, Italy native, Carlo joined the group in 2017 after completing his Ph.D at the University of Perugia, Italy, where he focused on the use of theoretical methods to study gold-catalyzed and spin-forbidden reactions. While finishing his Ph.D., Carlo worked at the Katholieke Universiteit Leuven in Belgium. Carlo’s current projects focus on developing metal organic frameworks (MOFs) for applications in catalysis and conductive materials. In his free time, Carlo enjoys trying new food and drinks, watching movies, swimming, biking, and playing bass guitar.
Two postdoctoral positions, Swanson Group at the University of Utah
- Multiscale Kinetic Modeling – focuses on extending the methods used in microkinetic modeling and systems biology to biomolecular processes. The ideal candidate should have some experience with kinetic modeling and at least exposure to MD simulations.
- Multiscale Modeling of Lipid Droplets – uses a suite of MD simulations, enhanced free energy sampling, and protein structure prediction to characterize how proteins target lipid droplets via their unique monolayer properties. The ideal candidate will have experience in MD simulations and enhanced free energy sampling.
For both positions, broader background and experience will be considered. Experience in or desire to learn machine learning would be especially welcome. Interested candidates should send a cover letter and CV to email@example.com.
Research Associate Position in Computational Chemistry, Michigan Technological University
- QM/MM modeling of transition metal-containing enzyme reaction mechanisms using ChemShell. AND/OR
- QM/MM MD (umbrella sampling or metadynamics) simulations of transition metal-containing enzyme mechanisms using CP2K.
Interested applicants should contact Dr. Karabencheva-Christova by e-mail: firstname.lastname@example.org and include a CV with a brief description of expertise and interests and the names of two references.
Junior Professor in Computational Materials Design for Chemical Energy Conversion at Technische Universität Braunschweig
The faculty is seeking candidates with an internationally outstanding research profile in theoretical chemistry, related but not restricted to the application of data-based methods (e.g., machine learning/deep learning, artificial intelligence) for the design of novel materials, with a focus on materials for chemical energy conversion (e.g., battery materials, photovoltaics, or catalysis). Collaboration within the priority research area “Mobility” of TU Braunschweig is expected. Responsibilities of the future professor include teaching at the undergraduate and graduate level of Chemistry as well as related areas. More can be found here.