Computational Chemistry

Tenure-stream appointment beginning July 1, 2027, focused on statistical mechanics applied to chemical systems. Develop theoretical and computational research on molecular structure, dynamics and collective behaviour, potentially including nonequilibrium phenomena, soft matter and biomolecular systems. Responsibilities include an independent externally funded research programme, teaching and service. A relevant PhD and evidence of research and teaching excellence are required. Salary: CAD 130,000–170,000 annually. Apply online with a cover letter, CV, research statement, teaching dossier, equity and diversity statement, and three referees. All materials and reference letters are due November 2, 2026, 11:59 p.m. Eastern Time. Canadians and permanent residents receive priority.

Study solid polymer electrolytes for metal-anode batteries using molecular dynamics and density-functional theory. The project connects chemical structure with electrolyte properties to identify improved battery materials. This master’s fellowship is based at the Institute of Chemistry in Araraquara and requires full-time dedication. Applications remain open until 30 November 2026. Follow the investigator’s application instructions to submit a Lattes CV, transcript and brief cover letter. A relevant undergraduate degree must be completed by the beginning of the January 2027 academic term; prior simulation experience is helpful but not compulsory.

The Simons Foundation invites two-year research proposals that use Empire AI's multinode GPU clusters to address fundamental questions in computational astrophysics and physics, computational biology, mathematics, neuroscience or computational physical chemistry. Proposals must develop and use foundational machine-learning models for the scientific problem; data curation may be considered only when the resulting data are used to solve scientific problems within the same proposal. The programme expects to fund six to twelve awards from a total budget of six million US dollars.

This webinar uses research on Chagas disease to explain how predicted protein structures can support drug discovery when experimental structures are unavailable. In the example study, AlphaFold models of Trypanosoma cruzi proteins supported a virtual screen of roughly 30,000 compounds; subsequent experiments identified antiparasitic activity in two approved drugs. The session introduces AlphaFold confidence measures and ways to obtain models, then follows a screening workflow through binding-pocket prediction, chemical-library selection, docking, and interpretation of results. It discusses difficulties encountered when docking against predicted structures and considerations for adapting the workflow to other targets. The material is intended for doctoral students, early-career researchers, and other drug-discovery professionals.

Develop machine-learning methods for generative drug discovery and turn research prototypes into usable scientific models. The role combines multimodal data, high-throughput experimentation and modern generative architectures, with work on model evaluation, production implementation and computational infrastructure. This is an on-site research position. Applications are available on the employer’s current vacancy page, with no fixed deadline stated.

Lead computational chemistry contributions to drug programmes from early discovery towards candidate selection. Combine structure-based and ligand-based design with AI predictions, work closely with machine-learning scientists and experimental teams, and guide external research activities. The role includes scientific leadership and mentoring. The employer’s exact vacancy is currently accepting applications without a stated fixed deadline.