The Department of Applied Physics is now looking for a
The Computational Electronic Structure Theory (CEST) group at Aalto University is hiring a Postdoctoral Researcher. In this position, you will work in a collaborative team of biomaterials researchers from Aalto University in Helsinki and Åbo Akademi in Turku. You will have a chance to make an impact on sustainable development by applying tools from artificial intelligence (AI) to biomaterials research. Our objective is to optimize the conversion of lignin and carbohydrates into functional materials and to understand the relation between the processing conditions and the structural properties of the materials.
Your role and goals
You will be developing and applying data science and machine learning approaches to optimize and better understand lignin-carbohydrate complexes for applications in, e.g., carbon fibers, thermoplastics or 3D printing. You will also be developing digitalization workflows for the biomaterials in the project. The position is part of an Academy of Finland funded research consortium. You will collaborate closely with the team members and coordinate the digitalization and optimization of the biomaterial samples and processing.
Your experience and ambitions
We welcome candidates with a PhD in chemistry, physics or materials science who are curious about applied machine learning in natural sciences. Prior machine learning or statistics experience is required. Biomaterials or polymer expertise, python knowledge or experience with digitalization are a bonus, but not a requirement. We seek colleagues who enjoy coding, scripting and analytics, and are keen to push the boundaries of data-driven materials science. This project requires creative thinking and programming, as well as technical expertise in machine learning and a broad understanding of biomaterials science. We further appreciate willingness to travel, teach and mentor, collaborate and communicate science.
What we offer
In the Computational Electronic Structure Theory (CEST) group, led by Prof. Patrick Rinke, we advance electronic structure theory and machine learning to pursue innovative applications towards future technologies. We are a multi-cultural and cross-disciplinary team, with complementary subgroups and talents. Our group is part of the FinnCERES Biomaterials flagship (https://www.finnceres.fi/
) and the Finnish Center for Artificial Intelligence (https://fcai.fi/
) and you will be embedded in a lively community at the crossroads of biomaterials AI research. You will train in machine learning applications with experienced developers, meet our global network of collaborators, join us at scientific meetings, help us organize research workshops and get involved in academic and diversity outreach. In combination with the academic development courses at Aalto University, we will help you grow a competitive and international career profile.
The fixed term contract is initially for two years. The annual workload of research and teaching staff at Aalto University is currently 1612 hours. Aalto University follows the salary system of Finnish universities. The starting salary for a Postdoctoral researcher is approx. 3700€/month. The contract includes Aalto University occupational healthcare. The primary workplace will be the Otaniemi Campus at Aalto University.
Ready to apply?
To apply for the position, please submit your application including the attachments mentioned below as one single PDF document in English through the link ’Apply’.
(1) Letter of motivation
(2) CV including list of publications
(3) Degree certificates and academic transcripts
(4) Contact details of at least two referees (or letters of recommendation, if already available)
The deadline for applications is Sept. 30 2021
. The position will be filled as soon as a suitable candidate is identified. For additional information, kindly contact Prof. Patrick Rinke (firstname.lastname (at) aalto.fi). Aalto University reserves the right for justified reasons to leave the position open, to extend the application period, reopen the application process, and to consider candidates who have not submitted applications during the application period.
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