PhD student in integrated AI/MD for the discovery of new materials

Project description
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.

PFAS replacement compounds hold the potential for significant environmental and human health benefits. Known for their stability, PFAS are persistent in nature, to the point that they have become ubiquitous in air, water, and soil. This raises serious concerns about their long-term ecological impact. By identifying and employing new compounds that possess equivalent or superior functionality to PFAS, we can significantly reduce their environmental footprint and mitigate the contamination risks associated with their widespread use. Furthermore, the engineering of PFAS replacement compounds is a vital step in safeguarding human health. Studies have linked exposure to PFAS with adverse health effects, though there are limited studies on the computational prediction of PFAS toxicity. This motivates the inclusion of health effects and toxicity prediction in any future tool developed for the discovery and design of novel PFAS replacement compounds. By identifying compounds that exhibit similar or enhanced properties in semiconductor manufacturing, such as excellent thermal stability and superior electrical insulation, we can not only meet the existing requirements of semiconductor manufacturing, but also pave the way for innovative breakthroughs in environmental and human health. However, in order to engineer PFAS replacements with desired property profiles, we must have an understanding of their molecular structures, chemical and thermal stabilities, hydrophobicity, charge transport mechanisms, and device architectures in which they are to be used. In addition, we cannot neglect the identification of potential degradation pathways under mild conditions, as without considering the full lifecycle of these materials, it is not possible to assess nor improve their environmental impact.

For this project, the PhD candidate will work on a novel, multi-modal deep learning approach can be used to predict the aforementioned properties, wherever data is readily available, thus setting the stage for the de novo design of non-toxic, PFAS replacement materials with
low environmental impact. Our proposed approach integrates molecular dynamics with experimental data to learn meaningful representations of the materials. We will highlight its utility on the prediction of PFAS toxicity, chemical and thermal stability, and degradation pathways. These properties are keenly dependent on both the molecular and crystal structures of the material.

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 PhD student position in the division include conducting doctoral research and coursework. By the end of the PhD, students will be able to identify novel research directions and design the appropriate computational experiments to answer key questions. Students are expected to effectively communicate the results of their research verbally and in writing, and will receive specific training towards building these skills. This position also includes teaching at Chalmers' undergraduate level, or performing other teaching duties corresponding to 20% of working hours. The appointment is a full-time temporary employment for a period of not more than 5 years, funded by the Intel-Merck AWASES program.

Read more about doctoral studies at Chalmers here .

Qualifications
To qualify as a PhD student, applicants must have a master's level degree corresponding to at least 240 higher education credits in Computer Science, Chemistry, Materials Science, or a related field. The applicant should have strong background and experience in Python programming and deep learning. Previous experience in Generative AI and/or molecular modeling is a merit, but not required.

The position requires sound verbal and written communication skills in English. If Swedish is not your native language, Chalmers offers Swedish courses.

Contract terms
Full-time temporary employment. The position is limited to a maximum of five years.

We offer
Chalmers offers a cultivating and inspiring working environment in the coastal city of Gothenburg .
Read more about working at Chalmers  and our benefits  for employees.

Chalmers aims to actively improve our gender balance. We work broadly with equality projects, for example the GENIE Initiative on gender equality for excellence . Equality and diversity are substantial foundations in all activities at Chalmers.

Application procedure
The application should be marked with reference number 20240036 and written in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support Zip files.

CV:(Please name the document: CV, Family name, reference number)
• CV
• Other, for example previous employments or leadership qualifications and positions of trust.
• Two references that we can contact.

Personal letter:(Please name the document as: Personal letter, Family name, ref.number)
1-3 pages where you:
• Introduce yourself
• Describe your previous experience of relevance for the position (e.g. education, thesis work and, if applicable, any other research activities)
• Describe your future goals and future research focus

Other documents:
• Copies of bachelor and/or master’s thesis.
• Attested copies and transcripts of completed education, grades and other certificates, e.g. TOEFL test results.

Please use the button at the foot of the page to reach the application form. 

Application deadline: 2024-05-25

For questions, please contact:
Assistant professor Rocío Mercado, Data Science & AI division,
[email protected], +45 76 854 7752

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

Chalmers University of Technology conducts research and education in engineering sciences, architecture, technology-related mathematical sciences, natural and nautical sciences, working in close collaboration with industry and society. The strategy for scientific excellence focuses on our six Areas of Advance; Energy, Health Engineering, Information and Communication Technology, Materials Science, Production and Transport. The aim is to make an active contribution to a sustainable future using the basic sciences as a foundation and innovation and entrepreneurship as the central driving forces. Chalmers has around 11,000 students and 3,000 employees. New knowledge and improved technology have characterised Chalmers since its foundation in 1829, completely in accordance with the will of William Chalmers and his motto: Avancez!
 

URL to this page
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