Junior PostDoc - Advanced Electron Nanoscopy (GAeN) Group

As a flagship research center in nanoscience and nanotechnology, our mission is to open and explore new frontiers of knowledge at the nanoscale and bring value to society through new understanding, capabilities, and innovation, while inspiring and providing broad training for the next generations of researchers. Our values are Commitment, Collaboration, and Transformation.

Our research lines focus on the newly discovered physical and chemical properties that arise from the behaviour of matter at the nanoscale. ICN2 has been awarded the Severo Ochoa Center of Excellence distinction for three consecutive periods (2014-2018, 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different areas of nanoscience and nanotechnology.

Job Title: Junior Postdoc

Department: Advanced Electron Nanoscopy (GAeN) Group

Description of Group/Project

This position focuses on AI-enhanced automation of transmission electron microscopy imaging and spectra acquisition and analysis, developing AI protocols based on ML, DL, and computer vision.

The candidate will join an interdisciplinary team working on a project that aims to develop in-situ characterization workflows for nanostructured catalysts. This role focuses on the AMaDE project, where the candidate will explore the limits of the different ex-situ and in-situ techniques to provide accurate information in comparison with data provided by the analysis of synchrotron radiation and STEM over different electrodes measured in the coupled electrochemical cell. Thus, detailed analysis of the many different instrumental parameters and experimental protocols employed in lab-based UHV-XPS and NAP-XPS, STEM EELS and synchrotron X-ray spectroscopy (XAFS, XANES) and diffraction (XRD) will be comprehensively carried out to unveil how morphology and chemical distribution of elements in the electrodes influence in data acquisition and interpretation comparison of the working principles of complementary probes in the very same systems under controlled protocol.

This systematic study will provide unprecedented information on applicability and data analysis depending on the specific aim or electrode. We will focus on integrating information obtained from X-ray photoemission and STEM-related analyses, along with morphology, electrochemical, and spectroscopic analyses.

Main Tasks and responsibilities

The candidate will join an interdisciplinary team and will develop in-situ characterization workflows for nanostructured catalysts.

This role focuses on the AMaDE project, where the candidate will explore the limits of the different ex-situ and in-situ techniques to provide accurate information in comparison with data provided by the analysis of synchrotron radiation and STEM over different electrodes measured in the coupled electrochemical cell. Thus, detailed analysis of the many different instrumental parameters and experimental protocols employed in lab-based UHV-XPS and NAP-XPS, STEM EELS and synchrotron X-ray spectroscopy (XAFS, XANES) and diffraction (XRD) will be comprehensively carried out to unveil how morphology and chemical distribution of elements in the electrodes influence in data acquisition and interpretation comparison of the working principles of complementary probes in the very same systems under controlled protocol.

This systematic study will provide unprecedented information on applicability and data analysis depending on the specific aim or electrode. We will focus on integrating information obtained from X-ray photoemission and STEM-related analyses, along with morphology, electrochemical, and spectroscopic analyses.

Requirements

• Education: Physics, Chemistry, Nanotechnology, or related fields.
• Knowledge: Expert in STEM, EELS, data analysis, and Python-based coding of AI ML/DL tools for automated STEM data analysis.
• Professional Experience: PhD in Materials Science, Physics, Chemistry, or similar.
• Personal Competences: Python programming, advanced hands-on STEM skills, plus advanced data analysis skills (EELS, GPA, EDX, atomic-resolution images).

Summary of conditions

• Full-time work (37,5h/week).
• Contract Length: Temporary; 12 months.
• Location: Bellaterra (Barcelona).
• Salary will depend on qualifications and demonstrated experience.
• Support for relocation issues.
• Life Insurance.
• Work-Life Balance and Flexibility with flexible work schedules.
• 28 holidays per year.
• Flexible compensation plan: tax advantages for contracting some products (health insurance, childcare, training, among others)
• Training activities: languages, mentoring programme, wellbeing programme.
• International environment.

Estimated Incorporation date: As soon as possible.

How to apply

All applications must be made via the ICN2 website and include the following:

1. A cover letter.
2. A full CV including contact details.
3. Reference letters or referee contacts

Deadline for applications: Applications will be continuously reviewed.

Advanced Electron Microscopy-Based Characterization Methods for Materials and Devices for Energy (AMADE). Project PID2023-019158OB-C43 funded by the Spanish Ministry of Science, Innovation and Universities (MICIU) / State Research Agency (AEI) / 10.13039/501100011033 and by the European Regional Development Fund (ERDF), European Union (EU).

Equal opportunities

At ICN2, we foster an inclusive and safe work environment, free from any form of discrimination-whether based on gender, sexual orientation, gender identity, age, origin, culture, religion, disability, or any other personal or social condition. We are committed to ensuring equal treatment and opportunities in all our processes, especially in recruitment, which is based solely on talent, experience, and ability. We implement proactive policies for inclusion and harassment prevention that reinforce our commitment to respect and fairness. If you share these values and are looking to grow in an open and diverse environment, ICN2 is ready to welcome you.

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