PhD candidates - Advanced Multifunctional Materials Science | MAB NOMATEN

Data ogłoszenia: 
25 paź 2021 - 10:15
Termin zbierania ofert: 
14 gru 2021 - 23:45


PhD candidates - Assistant Research (PL form “Asystent”)
    Advanced Multifunctional Materials Science

at NOMATEN Centre of Excellence, 
National Nuclear Research Centre (NCBJ),
Poland

 

NOMATEN Centre of Excellence (CoE) is formed through a scientific partnership between the National Centre for Nuclear Research (NCBJ-Poland), the French Alternative Energies and Atomic Energy Commission (CEA-France) and the Technical Research Centre of Finland (VTT-Finland) with joint financial support from the Foundation for Polish Science (FNP) and the European Commission. It is currently composed of 5 Research Groups and is directed by Mikko Alava. NOMATEN focuses research on the characterization, analysis and development of advanced multifunctional materials, specifically those designed to work in extreme conditions, with primary examples being radiation, high temperature and corrosion. We also conduct research on development of novel diagnostic and therapeutic approaches, based on radiopharmaceuticals, to defeat cancer disease. 

More info about NOMATEN CoE and the detailed project descriptions at http://nomaten.ncbj.gov.pl

Multiple positions exist on the PhD student levels in NOMATEN’s Research Groups:

1.    Complexity in Functional Materials: mechanical properties (yielding, time-dependent fracture) to the role and understanding of the microstructure in determining such properties. We also work on Machine Learning approaches in complex materials. 
Key words of importance are metal alloys, and High-Entropy Alloys in particular. 

Two example PhD projects are:

-    understanding materials’ deformation with material informatics. Plasticity in metals and metal alloys is a complex phenomenon. In this PhD project we apply imaging techniques and machine learning-like approaches to plasticity. You will do experiments in complex alloys, develop imaging techniques and apply Digital Image Correlation and then analyze the results with the aid of statistical physics models and ML tools. Examples of scientific challenges are the onset of plastic deformation [SP/MA] and deformation instabilities such as the Portevin-LeChatelier effect. In PLC, deformation bands nucleate and we want to study this avalanche phenomenon in modern alloys.

-    statistical mechanics of dislocations in complex alloys. Plasticity in metals and metal alloys depends at the fundamental level on the behavior of individual dislocations. In this project, you will develop the theory of how to understand the depinning and pinning of dislocations in disordered landscapes such as what is found in High Entropy Alloys. The goal is to bring the force of statistical physics of non-equilibrium systems to help to understand in detail how the chemical complexity and alloy composition influence the flow stress and the finite-temperature creep flow/velocity of dislocations.

Preferred background: Statistical mechanics, computational simulations, machine learning.
Contact person: prof. Mikko Alava (mikko.alava@ncbj.gov.pl)

2.    Functional properties group is studying impact of radiation damage on the mechanical and structural properties. Specific topics covering materials devoted to Gen. III+ and IV nuclear reactors like: stainless steels, ODS and HEAs, Al2O3 coatings, zirconium and nickel alloys will be proposed. Experiments will be conducted on ion damaged materials and at their working temperatures by using specific techniques like: nanoindentation, X-ray diffraction, tensile tests and Raman spectroscopy. Structural properties are determined by means of SEM/FIB/EBSD/EDS and TEM. 

Examples of PhD projects:

Studying mechanical properties and radiation resistance of fcc and bcc type materials. In the first case binary (NixFey) material will be investigated. In the second case, bcc-type refractory material will be studied. Both subjects will aim to understand mechanical and structural properties with special emphasis to radiation damage resistance. Prime interest will be given to  NixFey single crystals and WTaCrV or MoTaTiV systems deposited on Si film will be tested. 
Both PhDs will be working closely with structural and numerical groups at the CoE. It is known that these materials show unique radiation damage mechanism. Multiple atoms in random solid solutions are able to effectively reduce the mean free path of electrons, phonons and magnons. In so doing, it is good for delaying the formation of defects at the initial stage of radiation. However, there is limited information about the nature of such remarkable irradiation tolerance in HEAs, since the radiation damage mechanism of HEAs is different from that of traditional materials. The same situation occurs for bcc type materials. Both PhDs candidates will be using variety of methods like SEM/EBSD/EDX/FIB system, nanoindentation platforms, XRD and RBS/C. One of the goals of this work will be also to study high temperature nanomechanical properties of HEA. 

Preferred background: materials science, materials engineering, nuclear engineering, mechanics.
Contact person: dr. hab. Lukasz Kurpaska (lukasz.kurpaska@ncbj.gov.pl)

3.    Materials Structure, Informatics and Function: Emulating the behaviour and performance of materials in mechanical loading applications, using multiscale material simulations, machine learning methods and material characterization techniques. Methods of interest: Ab-Initio Density Functional Theory (DFT), Molecular Dynamics (MD), Discrete Dislocation Dynamics (DDD), and Continuum phenomenological Plasticity and Damage (CPD) modeling.

Three example PhD projects are:

-    the experimental method development for the elucidation of size effects in metals, in combination with in-situ microscopy data that may be used for digital image correlation purposes. Nanoindentation size effects and Digital Image Correlation in in-situ testing (Collaboration with EC Joint Research Center).
We are looking for a highly motivated PhD student (m/f/d) with an excellent Masters degree in physics, chemistry or a related field. The PhD student is expected to perform nanomechanics experiments at top EU facilities in Petten, Netherlands, and at NOMATEN, as well as perform data science tasks through the use of in-house developed Python software. Knowledge of experimental devices related to microscopy (SEM/TEM), will be highly valued. English communication skills are required. Previous programming experience and basic knowledge of Python are desired. The project will involve visits to the Micro-Characterization Laboratory in Petten, Netherlands, where nanomechanics experiments will be performed. 

-    the advancement of recently developed machine learning methods for materials discovery through the use of ab-initio data, for targeting light weightness as a key component Materials Discovery, Composition search and machine learning for lightweight Ti-based alloys: An Ab-Initio Approach (cross-industrial project). 
We are looking for a highly motivated PhD student (m/f/d) with an excellent Masters degree in physics, chemistry or a related field. Knowledge of electronic-structure theory. Previous programming experience and basic knowledge of electronic-structure programs such as QEspresso or VASP are desired. The project is focused on applications of electronic-structure calculations. 

-    the advancement of dimensional reduction approaches for machine learning in crystal plasticity applications, especially through the use of convolutional neural networks Dimensional reduction in crystal plasticity: Convolutional Neuronal Networks For Strain Maps (the person to be in contact with support team on machine learning, if chosen). Particular focus will be imposed on digital image correlation methods for strain evolution in mesoscale experimental mechanics. 
We are looking for a highly motivated PhD student (m/f/d) with an excellent Masters degree in physics, mechanics or a related field. Knowledge of machine learning and communication skills are required. Previous programming experience and basic knowledge of Python-based machine learning software such as Tensorflow, Keras, Sklearn are desired. The project is focused on alloy development applications and the in-house development of Materials Informatics software. 

Preferred background: Multiscale modeling, mechanical characterization, applied physics/math, machine learning.
Contact person: dr. Stefanos Papanikolaou (stefanos.papanikolaou@ncbj.gov.pl)

4.    Materials Characterization (“process-structure-property”): The main goal of the group is to conduct advanced characterization of novel multifunctional materials at the atomistic level using state-of-the art equipment. The focus is on studying the impact of high temperature, oxidizing atmosphere and radiation on the structural properties of materials using a wide range of techniques, including SEM/FIB/EBSD/EDX tools, TEM analysis, as well as advanced and in-situ X-ray diffraction and Raman spectroscopy. The structural characterization of the studied materials under extreme conditions fills the gap between simulations and functional properties of the material, by verification of the structural model, analysis of material response on various conditions occurring in real environments, analysis of mechanisms of damage accumulation and studies of microstructure influence on the mechanical properties.

PhD project: 

-    Understanding and explaining the processes taking place in materials designed to work in extreme conditions, with primary examples being radiation, high temperature and corrosion. The PhD student will be engaged in studies on the impact of radiation damage and high temperature on the structural properties of materials like: ODS and HEAs, Al2O3 coatings, zirconium, nickel alloys and polymers, working closely  with functional and numerical groups of the CoE. Specific topics on the development of 3-D structural models of materials and development of methods of materials structure validation using SEM/FIB/EBSD/TEM/Raman/XRD techniques will be proposed. 

Preferred background: Physics, materials science, materials engineering, nuclear engineering.
Contact person: dr. Iwona Jóźwik (Iwona.Jozwik@ncbj.gov.pl)

 

Our ambition is to build a team composed of world-leading researchers and young, highly motivated people who are passionate about multifunctional materials science. 
During their employment, the PhD candidates will be required to timely fulfil all the obligations connected with the process of obtaining the Doctoral degree in the chosen scientific disciplines (such as evaluation, passing exams, participating in lectures and other activities).

Location:
National Centre for Nuclear Research (NCBJ), ul.Andrzeja Sołtana 7, 05-400 Otwock, Poland
(Suburb of Warsaw, efficient and free dailybus transport service provided). 

Gross Salary:
7,000 per month (at current exchange rate 1,550 € per month); the details in each case depend on qualifications and experience, and the compensation is composed of the base salary and seniority addition, project bonus).
Read more about contributions in Poland at https://www.ncbj.gov.pl/en/hrcareer/contributions-poland

We offer:
2 years initial employment with extension after a positive evaluation.
Work in international networks with research institutes and industrial companies. 
Access to the research potential of NOMATEN’s three partners between NCBJ (Poland), CEA (France) and VTT (Finland). 
Some of the positions are for joint collaborative research with NOMATEN partners CEA (France) and VTT (Finland) and thus include extensive visits to the collaborating institution.
Travel funds for participation in conferences and collaboration, attractive working conditions, atmosphere of teamwork, family-friendly environment with flexible working hours. support of an experienced local team in legal, financial and organisational issues as well as logistic support and advice related to working in Poland - enabling smooth relocation and equal opportunities.

Required documents:

  • cover letter that explains the motivating factors for considering the position (max. 1 pp)
  • CV with complete publication list 
  • brief description of important scientific achievements and scientific outlook (max. 2 pp)
  • a list of 2 reference persons including their positions and contact details (e-mail address)
  • MSc diploma copy/scan:

The recruitment is open to candidates who, at the time of submitting their applications, do not have a diploma confirming MSc, but who have a fixed date for obtaining this title before the planned date of employment. In this case, it is necessary to provide documents that prove that.

  • as an attachment to your application please sign and enclose the following declaration: I agree to the processing of my personal data included in this application for the needs necessary to carry out the recruitment. 

Applications electronic form in English should be submitted to: magdalena.jedrkiewicz@ncbj.gov.pl

Position starts on: Feburary 1st, 2022 (at the earliest).
Candidates may be asked to provide additional documents. We reserve the right to contact only selected candidates and the right to inform about the decision to fill the post only to the selected candidate.
Candidates may be asked to provide additional documents. In the selection process, short-listed candidates will be interviewed in person or remotely. 

INFORMATION CLAUSE ON PERSONAL DATA PROCESSING:
1.    The controllers of the personal data processed during the recruitment process are: 
1)    National Centre for Nuclear Research, ul.Andrzeja Sołtana 7, 05-400 Otwock and
2)    Foundation for Polish Science, ul. I. Krasickiego 20/22, 02-611 Warszawa.
2.    The data protection officer can be contacted by using the following address:
1)    Personal Data Protection Officer, National Centre for Nuclear Research, 
Sołtana 7, 05-400 Otwock, Poland
2)    iod@ncbj.gov.pl
3.    Providing data contained in recruitment documents is a condition for applying for a job at NCBJ.
4.    Processing of the personal data for the purpose of filling the position listed in this announcement and to conduct subsequent recruitment is done on the basis of expressed consents. You have the right to withdraw your consent at any time, without affecting the lawfulness of the processing based on consent before its withdrawal.
5.    Your personal data will not be made available to other data recipients.
6.    Your personal data will not be transferred to a third country or to an international organization.
7.    No automated individual decision-making and profiling as referred in Article 22 (1) and (4) GDPR is done during recruitment conducted by NCBJ. This means that no decisions regarding job candidates are made automatically and that no job candidate profiles are made.
8.    In the case you have been unsuccessful in applying for the position listed in this announcement and you haven’t given consent to store the collected personal data in the NCBJ recruitment database, your data will be erased no later than 12 years from the completion of recruitment process, but no longer than the date of the end of the durability period of the project, which will find its basis in the provisions governing project financing.
9.    You have the right to access your personal data, request its rectification or erasure. Filing a request to erase data is tantamount to withdrawal from the recruitment process. You have also the right to request restriction of processing in cases specified in Article 18 GDPR.
10.    You have the right to lodge a complaint with a supervisory authority (President of the Office for Personal Data Protection) about unlawful processing of your personal data. The right to file a complaint only concerns the lawfulness of the processing of personal data, not the recruitment process.


The National Centre for Nuclear Research is awarded by “HR Excellence in Research. Recruitment is based on OTM-R system (Open, Transparent and Merit-based recruitment practices in Research Performing Organisations).

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857470 and Foundation for Polish Science International Research Agenda PLUS programme grant No MAB PLUS/2018/8 co-financed by the European Union under the European Regional Development Fund the Smart Growth Operational Programme.