阿尔托大学

Computational physics

项目介绍

The Complex Systems and Materials Group at The Department of Applied Physics is now looking for a

Doctoral candidate

to carry out theoretical and experimental research on the recently funded Academy of Finland project “Nature of highly disordered solids via advanced atom-probe tomography spatial visualization algorithms”.

Background

Atom Probe Tomography (APT) is a high-end microscopy technique that provides spatial information about material. It uses a high electric field to gradually evaporate the specimen, and further analyze the data with a position sensitive detector. However, the evaporating process is influenced by a number of stochastic effects, which can decrease the precision of further reconstruction. This is crucial in understanding the structure and properties of highly disordered solids such as high-entropy and amorphous alloys.

The aim of this project is to represent 3D atomic positions in a volume of high-entropy and amorphous alloy as a spatial probability density function, which can be used for further interpretation of microstructural peculiarities and their effect on material properties. This project will consider APT 3D reconstruction as a system identification to invoke building a mathematical model of a dynamic system from measured data using statistical approaches and advanced computational physics techniques including machine learning algorithms. As an expected research result, a new vision of atom probe data analysis will be developed and applied to understand the processing-microstructure-properties in relation to high-entropy and amorphous alloys, as an example of highly disordered solid-state matters. This highly multidisciplinary project will merge theory and experiment of APT, materials science, mathematics, and computational physics.

Your role and goals

You will work 3D spatial data reconstruction of gradually evaporated high-entropy and amorphous alloys as an inverse problem solution. This requires that a forward problem (i.e. evaporation model) be studied first. You will collect, reconstruct, and analyze real APT data to verify and he develop algorithms and implement it in machine learning routines.

Your experience and ambitions

Due to the multidisciplinary nature of this challenging research project, we are looking for brilliant, energetic, and ambitious candidates who are motivated and brave enough to get involved in this study that combines physics, mathematics, and computational science.  We require that applicants have obtained a MSc degree and an excellent study record in a relevant field (Physics, Computer Science, Materials Science…), with an interest in programming.  Experience in materials science and/or understanding of Probability and Statistics are of advantage. We are expecting a level of  independency, yet the  person should be able to work as part of a team.

What we offer

The candidate will join the Complex Systems and Materials (CSM) research group of the Department of Applied Physics at Aalto University. The CSM group applies statistical physics to a wide variety of cross-disciplinary topics, ranging from computer science to materials. To implement the high performance computations required in this project, the candidate will have access to the parallel Triton cluster with several tens of thousands of cores.

At the end of this project, the candidate will have acquired a diverse skill set to either continue their carrier in academia in a broad range of topics, or in industry with vast experience in advanced data science reinforced by high performance programming.

Aalto University follows the salary system of Finnish universities. The starting salary of a Ph.D. student is approximately 2550 €/month (gross), which increases with progress. Following the standard practice in the Department of Applied Physics, the contract will be made initially for two years, then extended to another two years after a successful mid-term progress review (total duration of Ph.D. studies is four years). The annual workload of research and teaching staff at Aalto University is currently 1612 hours. The contract includes Aalto University occupational healthcare. The primary workplace will be the Otaniemi Campus at Aalto University (Espoo, Finland).

Ready to apply?

If you want to join our community, please, submit your application through our online recruitment system by using the link on Aalto University’s web page (“Apply”) by 15th of November 2021. For additional information, kindly contact Prof. Mikko J. Alava (mikko.alava@aalto.fi) and Dr. Ivan Lomakin (ivan.lomakin@aalto.fi). To apply for the position, please submit your application including the attachments mentioned below as one single PDF document in English.

–   Letter of motivation
–   CV including list of publications
–   Degree certificates and academic transcripts
–   Contact details of at least two referees

Please note: Aalto University’s employees and visitors should apply for the position via our internal system Workday -> find jobs (not external aalto.fi webpage on open positions) by using their existing Workday user account.

The position will be filled as soon as a suitable candidate is identified. 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.

More about Aalto University:

Aalto.fi
twitter.com/aaltouniversity  
facebook.com/aaltouniversity
instagram.com/aaltouniversity

录取要求

  • We require that applicants have obtained a MSc degree and an excellent study record in a relevant field (Physics, Computer Science, Materials Science…), with an interest in programming.
  • We are expecting a level of independency, yet the person should be able to work as part of a team.

申请亮点

  • Experience in materials science and/or understanding of Probability and Statistics are of advantage.

项目概览

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访问项目链接 招生网站
北欧, 芬兰 所在地点
带薪项目 项目类别
截止日期 2021-11-15
阿尔托大学

院校简介

阿尔托大学位于芬兰,是一所古老而创新力强的世界著名高等学府。
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联系方式

电话: +358 9 47001

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