PhD Student Position in Generative models for Molecular Simulations

Molecular simulations provide an unprecedented level of detail for studying molecular processes. Molecular dynamics simulations find utility in various fields like drug discovery, materials science, and modeling chemical reactions. They are also applicable for simulating biological systems such as proteins and nucleic acids. However, these simulations are computationally intensive, severely limiting their practical application. In this project, the chosen candidate will utilize generative AI to expedite simulations, enabling a deeper understanding of complex molecular systems, particularly proteins.

Machine learning introduces exciting possibilities in the natural sciences, encompassing disciplines like physics, chemistry, and biology. Its potential applications range from designing new drugs to combat multi-resistant pathogens and deciphering the impact of genetic mutations on protein function to accelerating computer simulations for unraveling fundamental scientific phenomena and crafting efficient algorithms for upcoming quantum computers. The journey toward these applications harnesses the capabilities of deep learning to efficiently represent and process high-dimensional data while encapsulating the laws of nature and inherent symmetries.

To join our team, we are seeking a collaborative and self-motivated candidate with expertise in statistical mechanics, machine learning, or dynamical systems, preferably a combination thereof, acquired through academic coursework or completed projects (e.g., publications or software libraries).

PhD Project Description

As a Ph.D. student, you will join the graduate program offered by the Department of Computer Science and Engineering. Your principal role will revolve around advancing your doctoral studies, involving the following allocation of responsibilities:

80% Research and coursework
20% Service, which includes educational contributions

The research component encompasses the creation and execution of your scientific concepts, as well as the dissemination of your findings through oral presentations or written publications. Meanwhile, the service aspect entails fulfilling duties as a teaching assistant in undergraduate and master's level courses at Chalmers or participating in other departmental assignments.

Qualification

To be eligible for admission as a Ph.D. student, you need to hold a master's-level degree or a four-year bachelor's degree, equating to a minimum of 240 higher education credits in a pertinent field. This degree should have been conferred no later than the commencement of the position. Proficiency in spoken and written English is a prerequisite for this role. If Swedish is not your native language, you should have the capacity to teach in Swedish within two years, and Chalmers University provides Swedish language courses to support this.

How to Apply

The application is only to be submitted online by using the"Apply online" button below. Please be notified that application deadline is 15 October 2023. For further information related to the PhD scholarships, please visit the following Scholarship Link.

For questions, please contact:

Ass. Prof. Simon Olsson

simonols@chalmers.se