Oferty pracy w projektach finansowanych przez NCN

Nazwa jednostki: Faculty of Mechanical Engineering, Silesian University of Technology – Gliwice, Inne oferty z tego miasta »
Nazwa stanowiska: Students/PhD students (2 stanowiska)
Wymagania:

1. MSc degree in mechanical engineering, materials engineering, data science, chemistry, physics or a related
discipline
2. Strong background in at least four of the following scientific areas: (1) Feature engineering, (2) Uncertainty
Analysis, (3) Energy storage materials, (4) Phase Field Method (5) Statistics and optimization techniques (6)
Machine learning;
3. Programming skills in one of the following languages (e.g. Python, C++). Either highly experienced in using
TensorFlow/ PyTorch libraries to apply large language models (LLMs)/small language models (SLMs) for battery
dataset or proficient in utilizing the MOOSE Framework for finite element analysis of electrochemical systems if
moderately experienced in using Tensorflow/Pytorch libraries to train machine learning models. ;
4. Good command of spoken and written English language (IELTS: average of 6.5 or more; TOEFL: average of
79 or more, or B2 level or Equivalent);
5. The candidate has already completed the application procedure for the position associated with Ref. BAP-2024-
574.
6. Ability to work independently as well as work together in team.
7. Publication track record: The candidate(s) has/have authored scientific research article(s) in the field of
materials engineering or computer science or data science.

Opis zadań:

Lithium ion batteries (LIBs) are considered as the materials of the future when it comes to efficient energy storage. One of
the remaining problems limiting their lifespan is the formation of lithium dendrites. They are responsible for problems such
as short circuits, failures and fires, electrolyte decomposition, and loss of active lithium in these batteries. Dendrite
formation is an interfacial process spanning numerous length- and time scales. Despite decades of research, their
composition, structure and formation still present a significant conundrum. Achieving completely dendrite-free battery
interfaces can only be possible only the correct understanding of the fundamental mechanisms governing dendritic
evolution.
In the PhD research, you combine multi-physics finite-element simulations with the use of (generative) AI models. Within
the multi-physics finite-element model, the morphological evolution of the dendrites due to electrochemical reactions,
diffusion, convection and other physical effects will be simulated for two contexts – liquid (Type A) and solid (Type B)
electrolytes. The resulting computational datasets will be combined with experimental datasets to train attention
mechanism (AM) based generative and/or discriminative machine learning (ML) models. The list of ML models will
include but is not limited to MeshGraphNet, variational autoencoder, transformers etc. The objective is to generate in-
silico novel battery architecture or structures that are resistant to dendrite growth, as well as narrowing down the list of
features required for outlining the conditions favorable for suppressing the dendrite growth.
The main tasks for the PhD students are:
1. Perform multi-physics simulations,including phase field models for electrochemical migration in LIBs with
either Type A or Type B electrolyte, and tally them with available experimental data.
2. Quantify the dendrite evolution pattern in the computational model.
3. Blend the computational and experimental data associated with lithium ion battery to create a feature pool of
influential attributes.
4. Train generative and/or discriminative AI models with the datasets obtained in 3. Use these models to generate
structures/architecture or predict conditions that can ensure the prevention of dendrite growth in LIBs.
5. Contribute to the publications of peer-reviewed articles in reputed scientific journals;

Typ konkursu NCN: OPUS – ST
Termin składania ofert: 30 grudnia 2024, 17:00
Forma składania ofert: email
Warunki zatrudnienia:

Announcement of competition results: As soon as possible
During research stay for first 24 months at KU Leuven :
Place of work: Department of Materials Engineering, KU Leuven, Leuven, Belgium
Duration of scholarship: 24 months
Working hours: Full time
Further Information: Information on salary and working conditions under a doctoral scholarship at KU Leuven:
https://www.kuleuven.be/personeel/jobsite/en/phd/phd-information#working-conditions
During research stay for remaining 24 months at Silesian University of Technology:
Place of work: Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice,Poland
Duration of scholarship: 24 months
Working hours: Full time (40 h/week).
Date of commencement of employment: As soon as possible.
Further Information:
Detailed information about NCN scholarships is available at
https://www.ncn.gov.pl/sites/default/files/pliki/uchwalyrady/2022/uchwala124_2022-zal1_ang.pdf . We kindly request the
applicant to read the NCN's announcement on exemption from income tax from the NCN research scholarship:
https://www.ncn.gov.pl/en/aktualnosci/2021-12-30-stypendia-ncn-podatk

Dodatkowe informacje:

The Faculty of Mechanical Engineering at Silesian University of Technology (Gliwice, Poland) announces scholarship
competition for two students/PhD students. This announcement (PFML-S-2024-4) by the Faculty of Mechanical
Engineering at Silesian University of Technology (Gliwice, Poland) is connected to a corresponding announcement (Ref.
BAP-2024-574) communicated earlier by the Department of Materials Engineering, KU Leuven (Leuven, Belgium). The
successful applicants of PFML-S-2024-4 will participate in the research project titled: "Learning the Physics of Dendrite
Growth in Lithium-Ion Batteries: An Attention Mechanism Approach for Prevention and Mitigation
(DENDRITEPHASE)”. The DENDRITEPHASE research project is jointly funded by the Narodowe Centrum Nauki
(NCN) and Fonds voor Wetenschappelijk Onderzoek -Vlaanderen (FWO).
The scholarship competition in connection to the research stay at Gliwice is represented by the reference ( PFML-S-2024-
4) . The announcement related to the research stay at Leuven was represented by the reference (BAP-2024-574).

These scholarship positions are related to joint PhD positions between the two universities. Therefore, the candidates applying for the positions Ref.
PFML-S-2024-4 at the Faculty of Mechanical Engineering, Silesian University of Technology, Gliwice are expected to include a statement in their application letter confirming that they have already submitted their application documents in
response to the announcement Ref. BAP-2024-574 published earlier by Department of Materials Engineering, KU Leuven,
Leuven.
i. Application procedure (Ref. PFML-S-2024-4) :
The application should contain the following documents/information:
1. CV including the following information (list of scientific achievements, doi of publications , conference presentations,
information about authored preprints or draft manuscripts in review, link to github repositories of published codes, any ppt
slides or pdf containing information about skills related to large/small/mini language models or phase field simulations) ;
2. Copy of the MSc diploma or equivalent document or a document confirming the last year of master’s studies;
3. Copy of the MSc/ BSc thesis abstract;
4. Application letter or letter of motivation (maximum 1 page).
5. Acronym for reference of this position (Reference: PFML-S-2024-4).
In addition to the above documents, please prepare a document consisting of the following statement: "I consent to the
processing of my personal data for the purpose of recruitment in accordance with Art. 6 sec. 1 letter a of the Regulation of
the European Parliament and of the Council (EU) 2016/679 of 27 April 2016 on the protection of individuals with regard to
the processing of personal data and on the free movement of such data, and repealing Directive 95/46 /EC (general
regulation on data protection ). "
Application document (all of the documents combined together in a single pdf file) in English should be sent electronically
to the Co-PIs of the project Dr. Anil Kunwar (e-mail address:
anil.kunwar@polsl.pl ) and Professor Nele Moelans (e-
mail address: nele.moelans@kuleuven.be ). It is recommended to include the job reference (Reference: PFML-S-
2024-4) in the subject of the email message.

Data dodania ogłoszenia: 2024-12-19 18:11:53