Postdoctoral researcher in molecular simulation and machine learning

Deep generative models (DGMs) are poised to transform our approach to biomolecular engineering by designing molecules with desired properties from scratch so as to minimize experimental screening. Methods such as DGMs can be integrated with traditional molecular simulation approaches to aid computational chemists in the design and selection of promising new drug candidates. Nonetheless, they have seen limited application to multi-target therapeutic modalities and multi-modal data.

In this project, the candidate will focus on the development of novel AI tools for the accurate prediction of cell permeability in multi-target therapeutic modalities. Such an approach will be vital to the success of next-generation DGMs which are able to engineer cell permeable modalities. To this end, the candidate will have the opportunity to bridge their background in molecular simulations with the latest developments in machine learning. While the focus of this project is on deep learning and method development, the ideal candidate comes from a computational chemistry or related background, and is interested in developing their machine learning expertise through this interdisciplinary research project.

This project is funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). WASP is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.

Information about the division and the department
The AI Laboratory for Biomolecular Engineering (AIBE) is based in the Data Science & AI (DSAI) division in the Department of Computer Science and Engineering (CSE). Led by Dr. Rocío Mercado, our group uses methods from machine learning and the life sciences to understand how molecules interact to form complex systems, and how we can use these insights to engineer molecular systems for therapeutic applications. We are currently focused on applying our computational tools to improving the understanding and design of multi-target therapeutic modalities like PROTACs and molecular glues, which require large networks of proteins to come together and cooperate for the desired therapeutic effect to occur. We interact closely with leading academic and industrial groups in computational chemistry, bioinformatics, and computer science. In AIBE, we seek to create a vibrant and collaborative environment where students and postdocs are supported in their pursuit of challenging research questions at the forefront of machine learning and the life sciences.

The CSE department is a joint department at Chalmers University of Technology and the University of Gothenburg, with activities on two campuses in the city of Gothenburg. The department is divided into four divisions, and employs around 270 people from over 30 countries. Research in the department has a wide span, from theoretical foundations to applied systems development. We provide high quality education at the bachelor's, master's, and graduate levels, offering over 120 courses each year. We also have extensive national and international collaborations with academia, industry and society.

Our aim is to actively improve our gender balance in both our department and division. We therefore strongly encourage applicants from historically-excluded groups to our positions, such as women and non-binary individuals. As an employee of Chalmers and CSE department, students are given the opportunity to contribute to our active work within the field of equality and diversity.

Major responsibilities
The major responsibilities for a postdoctoral researcher in the division include designing and carrying out cutting-edge research projects. Incoming postdocs should be able to identify novel research directions and design the appropriate computational experiments to answer those key questions, while being motivated to build expertise in an area complementary to their PhD. Postdocs are expected to effectively communicate the results of their research verbally and in writing, and will receive specific training towards honing these skills if desired.

While the working time of postdoctoral researchers is mainly devoted to research, this position also includes teaching at Chalmers' undergraduate level, or performing other teaching duties corresponding to 20% of working hours (e.g., mentorship of master students). The appointment is a full-time employment for a period of not more than 2 years (1+1), funded by the prestigious Wallenberg Autonomous Systems Program (WASP).

Qualifications
A PhD in computational chemistry, biophysics, molecular biology, or related fields is required before the start of the appointment. Previous research experience in molecular dynamics simulations of proteins strongly preferred. The applicant should have previous programming experience, preferably in Python. Previous experience in the simulation of protein-protein interactions, membrane proteins, or PROTAC systems strongly desirable. Previous research experience in machine learning is also a merit, but not required.

Contract terms
This postdoc position is a full-time temporary employment for two years.

For more information about what we offer and the application procedure, please visit Chalmers Webpage.
See link: Postdoctoral researcher in molecular simulation and machine learning


*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***