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Annalisa Manera

Professor

ETH Zürich

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Switzerland

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Research Interests

Energy Engineering

10%

Artificial Intelligence

10%

Physics

40%

Machine Learning

20%

Fluid-structure Interaction

20%

Materials Science

20%

Computational Chemistry

20%

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Positions4

Publisher
source

Annalisa Manera

University Name
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ETH Zürich

Postdoctoral Research Fellow in Aqueous Chemistry and Thermochemical Modelling

The Nuclear Safety and Multiphase Flows Laboratory (NS-ECMFL) at ETH Zürich, led by Professor Annalisa Manera, is seeking a Postdoctoral Research Fellow to advance research in aqueous chemistry and thermochemical modelling within the context of nuclear safety. The laboratory, part of the Department of Mechanical and Process Engineering, specializes in experimental and computational studies of multiphase flows and thermal-hydraulic phenomena in nuclear systems. The group is renowned for developing advanced measurement techniques, conducting high-resolution experiments, and performing multi-scale, multi-physics simulations to improve nuclear reactor safety and efficiency. This postdoctoral position is part of an international project focused on predicting and managing liquid source terms in severe nuclear accidents. ETH Zürich plays a pivotal role in gathering essential data, improving understanding of liquid source term phenomena, and developing simulation tools for nuclear safety applications. The project involves collaboration with PSI and reviews data from previous projects, including leaching tests of Chornobyl's lava and Fukushima-related research, with a particular emphasis on thermodynamic data for caesium and strontium compounds. The successful candidate will contribute to the development and validation of new simulation models, review thermodynamic solvers and databases, and integrate new models into severe accident codes to predict the behaviour of fission products in liquid phases. Key responsibilities include extending and validating thermodynamic databases for aqueous systems, analyzing experimental data, developing and validating simulation models with a focus on thermodynamic and geochemical tools, implementing models into simulation codes, and collaborating with project partners from leading research institutions. The role also involves presenting research findings at international conferences, disseminating project results, and working closely with colleagues in both computational and experimental domains. Applicants should hold a Ph.D. in Nuclear Engineering, Physics, Chemical Engineering, or a related field, and possess experience with computational chemistry and simulation tools. Knowledge of nuclear safety, severe accident analysis, and radioactive material behaviour is advantageous. Proficiency in programming languages such as C++ and Python is required, along with strong communication skills, creativity, independence, and a collaborative attitude. ETH Zürich values diversity, sustainability, and an inclusive culture, offering numerous benefits including professional development opportunities, public transport season tickets, childcare, and attractive pension schemes. Applications must be submitted online via the ETH Zürich application portal, including a CV, cover letter, and reference letters. For further information, candidates may contact Dr. Sergii Nichenko at [email protected] (no applications via email). Join ETH Zürich to contribute to impactful research and positive societal change in a world-class academic environment.

Publisher
source

Annalisa Manera

University Name
.

ETH Zürich

Postdoctoral Research Fellow in Aqueous Chemistry and Thermochemical Modelling

The Nuclear Safety and Multiphase Flows Laboratory (NS-ECMFL) at ETH Zürich, led by Prof. Dr. Annalisa Manera, invites applications for a Postdoctoral Research Fellow in aqueous chemistry and thermochemical modelling. The lab, part of the Department of Mechanical and Process Engineering, is renowned for its expertise in experimental and computational studies of multiphase flows and thermal-hydraulic phenomena in nuclear systems. The group develops advanced measurement techniques, conducts high-resolution experiments, and performs multi-scale, multi-physics simulations to enhance nuclear reactor safety and efficiency. This postdoctoral position is part of an international project focused on predicting and managing liquid source terms in severe nuclear accidents. The project aims to gather essential data, improve understanding of liquid source term phenomena, and develop simulation tools for nuclear safety applications. ETH Zürich collaborates with PSI and other leading institutions, reviewing data from Chornobyl and Fukushima, with a particular emphasis on thermodynamic data for caesium and strontium compounds. The role involves extending and validating thermodynamic databases, analyzing experimental data, developing and validating simulation models, and implementing these models into simulation codes and algorithms. The successful candidate will also present research findings at international conferences, contribute to project dissemination, and collaborate with both computational and experimental colleagues. Applicants should hold a Ph.D. in Nuclear Engineering, Physics, Chemical Engineering, or a related field, and have experience with computational chemistry and simulation tools. Knowledge of nuclear safety, severe accident analysis, and radioactive material behaviour is advantageous. Proficiency in C++ and Python is required, along with strong communication skills and a collaborative, solution-oriented mindset. ETH Zürich offers a dynamic, inclusive, and diverse research environment, with numerous professional development opportunities and attractive benefits, including public transport passes, sports facilities, childcare, and pension plans. Applications must be submitted online via the ETH Zurich application portal, including a CV, cover letter, and reference letters. For further information, candidates may contact Dr. Sergii Nichenko at [email protected] (no applications via email). Join ETH Zürich and contribute to impactful research in nuclear safety and thermochemical modelling.

Publisher
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Annalisa Manera

University Name
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ETH Zürich

Marie Skłodowska-Curie Doctoral Training Network – Coupled Problems for Decarbonization in Industry and Power Generation (COMBINE)

The Marie Skłodowska-Curie Doctoral Training Network (COMBINE) at ETH Zürich offers a unique PhD opportunity focused on coupled problems for decarbonization in industry and power generation. This interdisciplinary network unites 17 academic institutions and 14 industrial partners across Europe, addressing key challenges in fluid structure interaction (FSI) relevant to energy, process, and materials engineering. The featured project centers on high-resolution experimental measurements of vibrations in rods and rod bundles using ensemble average high-resolution gamma tomography. The research leverages ETH Zürich's state-of-the-art gamma tomography system, aiming to advance tomographic techniques for evaluating vibrations in rod bundles. The project involves constructing experimental setups, conducting measurements with gamma tomography, and applying AI-assisted reconstruction and imaging techniques. Machine learning will be used for noise reduction and data enhancement, providing a cutting-edge approach to experimental engineering research. Applicants should have a Master's degree in engineering, physics, applied mathematics, computer science, or a closely related field. Essential skills include a strong background in mathematics and machine learning, programming proficiency (Python, C++ or similar), and experience with data processing and model interpretation. The ability to work independently and collaboratively in an international research environment, along with good written and spoken English, is required. Hands-on experience with experimental setups and basic electronics is advantageous. Successful candidates will benefit from an excellent research environment, supervision by internationally recognized academic and industrial experts, network-wide training events, and local courses in technical and transferable skills. International secondments at partner institutions across Europe are included. The research group offices and experimental laboratory are located at Paul Scherrer Institute (PSI), Villigen, Switzerland. ETH Zürich is renowned for its commitment to diversity, sustainability, and excellence in science and technology. The university promotes equality of opportunity and a climate-neutral future, providing a supportive environment for all staff and students. Applications must be submitted online via the ETH Zürich application portal. Required documents include CV, transcripts, and cover letter. For further information about the Laboratory of Nuclear Systems and Multiphase Flows, visit the ETH Zürich website. Questions regarding the position can be directed to Prof. Annalisa Manera ([email protected]), but applications via email or postal services will not be considered. This position offers a competitive salary as part of the Marie Skłodowska-Curie Doctoral Training Network, with comprehensive training and international research opportunities. Join a vibrant, interdisciplinary team working towards innovative solutions for decarbonization in industry and power generation.

Publisher
source

Annalisa Manera

University Name
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ETH Zürich

PhD in Vibrations of Rods and Rod Bundles Using High-Resolution Gamma Tomography

ETH Zürich is recruiting a motivated PhD student for the Marie Skłodowska-Curie Doctoral Training Network COMBINE (Coupled Problems for Decarbonization in Industry and Power Generation). The project focuses on high-resolution experimental measurements of vibrations in rods and rod bundles using an in-house high-resolution gamma tomography system . The research sits at the intersection of mechanical engineering , physics , computer science , and materials science , with strong links to fluid-structure interaction , nuclear safety , experimental methods, sensor technologies, data analysis, and AI/ML for engineering applications. Planned work includes building a setup with a single rod and rod bundle, measuring vibrating rods/rod-bundles with gamma tomography, and developing AI-assisted reconstruction and imaging methods using sensor and tomography data. Machine learning will be used for noise reduction and data enhancement. Eligibility highlights: a Master's degree in engineering, physics, applied mathematics, computer science, or a closely related field; strong mathematics and machine learning background; programming skills in Python, C++, or similar; experience with data processing and model interpretation; good English; and an advantage for hands-on experimental setup and basic electronics knowledge. Funding: the position is part of a Marie Skłodowska-Curie Doctoral Training Network and offers a competitive salary, interdisciplinary training, and secondments at academic and industrial partner institutions across Europe. Application: submit a CV, transcripts, and cover letter via the ETH Zürich online application portal. Applications by email or postal mail are not considered. Questions should be directed to Prof. Annalisa Manera at [email protected].