Postdoc in molecular modeling for data-driven materials discovery
Chalmers Tekniska Högskola AB / Högskolejobb / Göteborg
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Per- and poly-fluoroalkyl substances (PFAS) have long been used in various industrial applications due to their favorable chemical and thermal stability, water resistance, and electrical insulation. In particular, they are widely used in the process of semi-conductor manufacturing; however, due to recent evidence of their adverse environmental and health effects, regulatory agencies are increasingly tightening restrictions on PFAS usage and pushing for their eventual phase-out, spurring an urgent need for alternative compounds. As such, semiconductor manufacturers face the imminent need to align their manufacturing practices with these strict, new regulations. The development of new PFAS replacements which address the potential risks associated with them while maintaining their favorable chemical properties is one of the crucial problems facing humanity in the next decade.
To engineer PFAS replacements for use in surfactants for etching solutions during semiconductor manufacturing, we must better understand what factors lead to their unique molecular properties, namely, their low surface tension in liquid-liquid interfaces. Because of this, PFAS are ideal surfactants which enable surface coat uniformity and improved line roughness during the semiconductor manufacturing process. However, surface tension is particularly challenging to model computationally due to the long time-scales and sensitivity to simulation parameters needed. Obtaining a better understanding of how molecular structure is related to surface tension can thus help design PFAS replacements with equivalent or better wetting characteristics than the currently used PFAS, but without the detrimental health and environmental effects.
For this project, the postdoctoral researcher will develop a molecular dynamics (MD) approach to modelling crucial chemical properties of PFAS replacement materials with a special focus on predicting surface tension. Surface tension can then be used to predict other relevant properties to surfactant development, such as the critical micelle concentration and wetting ability. This approach will be used to create an ML-ready dataset, which can be used to build various AI-driven QSPR models for computational prediction of these material properties. With a quick way to accurately predict surface tension for new molecules, the QSPR model can be used as a reward function for a reinforcement learning agent aimed at designing potential PFAS-replacement compounds.
This project is part of a collaboration between the AI Laboratory for Biomolecular Engineering, led by Dr. Rocío Mercado at Chalmers, and the Intel-Merck AWASES Program, a joint academic research center between Intel and Merck with the goal of accelerating sustainable semiconductor manufacturing processes.
Information about the division and the department
The AI Laboratory for Biomolecular Engineering (AIBE) is based in the division of Data Science & AI (DSAI) 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 molecular systems for drug discovery and materials applications. We interact closely with leading academic and industrial groups in computational chemistry, bioinformatics, materials science, 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 molecular 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 3 years (2+1), funded by the Intel-Merck AWASES program.
For more information about qualifications, contract terms, what we offer and the application procedure, please visit Chalmers webpage.
See link: Postdoc in molecular modeling for data-driven materials discovery
Ersättning Lön enligt överenskommelse
Så ansöker du Sista dag att ansöka är 2024-03-05
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Arbetsgivare Chalmers Tekniska Högskola AB (org.nr 556479-5598)
Jobbnummer 8427273
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