Helmholtz-Zentrum Hereon, Geesthacht, Germany, with PhD degree awarded by Kiel University (CAU), Germany.

Uppsala Universitet, Sweden and Quintus Technologies AB, Västerås, Sweden

This PhD position is part of the EU-funded project Print4Life, a Marie Sklodowska-Curie (MSCA) doctoral network led by Prof. Cecilia Persson, Uppsala University.

This network has 8 host institutions hiring doctoral candidates: Uppsala University, Universitat Politecnica de Catalunya, Dublin City University, Universidade do Minho, Helmholtz-Zentrum Hereon, Hydrumedical SA, Brinter AM Technologies Oy, ETH Zürich; and 6 additional partners: FHNW, PBC Ltd, XapHe, Quintus Technologies AB, CAU (Kiel) and Region Uppsala (Uppsala University Hospital). The network aims to provide Advanced Research Training for Additive Manufacturing of the Biomaterials and Tissues of the Future. You will be one of 17 doctoral students across the entire network, and one of 3 doctoral candidates based at Helmholtz-Zentrum Hereon.

You focus on developing your PhD project In silico optimization of PBF-LB manufactured Mg alloys. There is a plethora of studies on the processing, mechanical and corrosion properties of Mg-based alloys, mostly limited to the alloys produced via traditional casting, extrusion and forging. Reports on rationalisation of processing-structure-property relationships (PSPR) additively manufactured (PBF-LB) Mg alloys are still scarce. In the meantime, the variation of processing parameters (laser power, spot size, hatch distance, scanning speed, layer thickness, scan strategy and subsequent heat-treatment) has a significant effect on the microstructure (grain size, alloying elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant parameters adopting a design of experiments strategy, a probabilistic (e.g. Gaussian Process Regression) model to describe the relationship between process parameters and material properties will be developed and subsequently exposed to Bayesian optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g. random forest (RF), artificial neural network (ANN)) will be applied using the parameters of strongest influence on the target properties. Moreover, the obtained data will be fed into a generative pre-trained transformer model (e.g. ChatGPT) to probe the potential of in-context learning for process optimization. The robustness of the model is assessed by experimental validation. The objectives are i) to produce Mg-0.1Ca alloy at varying PBF-LB fabrication process parameters (at Uppsala University), collect experimental data on microstructure, mechanical and corrosion properties; ii) determine, using sensitivity analysis, impact of the individual process parameters on the target properties and develop predictive machine learning model; iii) based on the machine learning algorithms, develop PBF-LB Mg alloy with defined microstructure, improved mechanical and corrosion properties.

Research stays are planned at: Uppsala Universitet, Sweden, and Quintus Technologies AB, Västerås, Sweden

Supervisors: Dr. Christian Feiler, Dr. Sviatlana Lamaka, Prof. Mikhail Zheludkevich

To be eligible as a MSCA (Marie Skłodowska-Curie Actions) doctoral candidate, you must i) not have a doctoral degree at the date of recruitment, ii) not be enrolled in a doctoral program, and iii) comply with a mobility rule requiring you to not have resided or carried out your main activity (work, studies, etc.) in the host country (Germany) for more than 12 months in the 36 months prior to recruitment (i.e. this includes online activities).

Master’s degree and documented experience and knowledge in either of the following: materials science, biomedical engineering, chemistry, chemical engineering, materials engineering, engineering physics, applied mathematics, computational materials science or a related field, or other education considered equivalent to these qualifications.
We require good oral and written skills in English.

It is advantageous to have experience in one or more of the following areas:

Great emphasis is put on personal qualities such as the ability to independently plan and carry out work. You have a good ability to work towards set goals and you work focused to achieve them. In this, you have an ability to focus, even during periods of high workload. You have good communication skills, where through this quality you cooperate well with others. You also have a good ability to write academic texts.

The application should be written in English.

PhD students employed through the Marie Sklodowska Curie Doctoral Network will receive a salary in accordance with the Marie Curie regulations for doctoral candidates for the time of their fellowship (36 months). Taking into account the living allowance (with the coefficient of 101.2% for Germany), mobility and family allowances (when eligible) and subtracting the employer contributions, the gross salary in Germany is 3719,13 eur/month (or 4233,93 eur/month with family allowance if eligible). Social security contributions and income tax will be deducted from this gross salary before payment of the net salary to the doctoral candidate.

This employment is a temporary position. Scope of employment 100 %. Starting date 1st March 2026 or as agreed upon. Placement: Geesthacht (near Hamburg), Germany.

Severely disabled persons and those equaling severely disabled persons who are equally suitable for the position will be considered preferentially within the framework of legal requirements.