National University of Sciences and Technology

Aalto University is where science and art meet technology and business. We shape a sustainable future by making research breakthroughs in and across our disciplines, sparking the game changers of tomorrow and creating novel solutions to major global challenges. Our community is made up of 120 nationalities, 14 000 students, 400 professors and close to 5000 faculty and staff working on our dynamic campus in Espoo, Greater Helsinki, Finland. Diversity is part of who we are, and we actively work to ensure our community’s diversity and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The School of Engineering drives science and innovation in industrial and built environment technologies. We are committed to educating a new generation of experts who combine technical excellence with a deep understanding of sustainable development in shaping societies. Our research focuses on sustainable built environment, mechanics and materials, multidisciplinary energy technology, and design and implementation of technical systems. The strength of our school lies in close collaboration with stakeholders across research and education. About 45 doctoral candidates and 350 master’s students graduate from the school every year. The school is home to 700 staff members, including 70 professors. To learn more, please visit eng.aalto.fi. We are now looking for a Doctoral Researcher in Structural Health Monitoring and Condition Assessment. We are seeking a highly motivated Doctoral Researcher in Structural Health Monitoring and Condition Assessment of Bridges to join the Structural and Bridge Engineering Research Group in the Department of Civil Engineering at Aalto University. Your role and goals The successful candidate will conduct doctoral research at the intersection of structural engineering and artificial intelligence. The research will focus on advancing methods for monitoring, assessing, and managing civil infrastructure through innovative sensing technologies and data-driven approaches. The research topics include: Structural health monitoring of bridges and other civil infrastructure Finite element analysis Laboratory and field-based structural testing and data acquisition Direct and indirect sensing methods for bridges, including remote sensing techniques Machine learning and artificial intelligence for infrastructure condition assessment Damage detection and software/application development for structural assessment The doctoral degree will be awarded upon successful completion of advanced doctoral coursework (30 ECTS), preparation of an article-based doctoral dissertation, and a successful public defence of the thesis. During the duration of the doctoral studies, the researcher will be expected to present publish in leading academic journals and high-level scientific conferences, engage in teaching-related duties, actively participate in the research group activities, assist in the preparation of new research proposals, and help to advise master’s students during the preparation of their final project. The research will involve extensive large-scale structural testing in both laboratory and field environments, as well as the development and application of direct and indirect (e.g., drive-by) structural health monitoring approaches. The researcher will collaborate closely with external stakeholders from industry and public organizations, requiring regular communication and engagement. The primary workplace is Aalto University’s Otaniemi campus, and the role requires regular on-site presence. Your network and team The Doctoral Researcher will be a member of the Structural and Bridge Engineering Research team at the Department of Civil Engineering. The doctoral studies and research will be supervised by Professor Weiwei Lin. Your experience and ambitions We are looking for a highly motivated candidate with a strong background in structural engineering and a demonstrated interest in structural health monitoring, structural testing, and artificial intelligence. Applicants should hold a master’s degree with excellent academic performance in Civil Engineering, Mechanical Engineering, or a closely related field. Additional studies or research experience in computer science, data science, or artificial intelligence will be considered an advantage. Excellent written and fluent verbal communication skills in English are essential. Finnish proficiency is considered as an advantage. The successful candidate should enjoy working in an international and interdisciplinary research environment and be eager to engage with both methodological and applied research challenges. Strong interpersonal and communication skills are highly valued. The ability to work both independently and collaboratively within a research team is essential. Previous knowledge and experience in the following will be considered an advantage: Experience in structural testing, sensing technologies, and data processing Experience in scientific writing and academic publishing Programming skills and experience with MATLAB, Python, or similar tools Familiarity with machine learning and deep learning methods Applicants must fulfill the eligibility and admission criteria for Aalto’s Doctoral Programme in Engineering as specified Aalto Doctoral Programme in Engineering | Aalto University. Do note that the applicants are not expected to apply to the Aalto Doctoral Programme at this point – only the successful candidate will do this at the next application window. What we offer You will join a dynamic and supportive research group that values collaboration, curiosity, innovation, and academic excellence. The position provides outstanding opportunities for professional development and interdisciplinary learning at the interface of structural engineering, sensing technologies, and artificial intelligence. Within Aalto’s broader community of students, researchers and other professionals, you will receive support to build your career and to reach your professional goals. Our vast array of professional development opportunities means you will grow and learn, having the chance to participate actively in staff trainings and development projects based on your interests and needs. The preferred starting date is Autumn 2026. The initial contract period is 2 years, evaluated by a compulsory midterm review during the second year. The salary range, terms, and conditions are based on a collective bargaining agreement among Finnish universities. The starting salary is 3143 €/month (gross), and increases as the Doctoral Researcher progresses in their research and studies. Join us! To apply, please share your application with us through our recruitment site ("Apply now!”) at the latest by 31st August 2026. Applications submitted by email will not be accepted. We encourage early applications, as candidates may be invited for interviews during the application period and the position may be filled before the application deadline. Please note that the position may remain unfilled if no suitable candidate is identified. Please include the following attachments: Motivation letter with the applicant’s contact information and an indication of the preferred starting date (max 1 page) CV including details of all academic outputs and publications (if available) Contact information for two referees (with email addresses) who may be contacted to obtain a recommendation (max 2 pages) Summary of the applicant’s master’s dissertation (max 1 page) Master's degree certificate and transcript of records, including an explanation of the grading scale. Students in the final year of their Master’s programme are welcome to apply, but the expected graduation date must be clearly stated in the application. Candidates must have completed the master’s degree before the PhD contract begins. Please note: Aalto University’s employees should apply for the position via our internal HR system Workday (Internal Jobs) by using their existing Workday user account (not via the external webpage for open positions). If you are a student or visitor at Aalto University, please apply with your personal email address (not aalto.fi) via Aalto University open positions For more information about the role or application process, please contact Prof. Weiwei Lin (weiwei.lin@aalto.fi). On questions related to the recruitment system, please contact hr-eng@aalto.fi. Want to know more about us and your future colleagues? You can watch these videos: This is Aalto University! Aalto University – Towards a better world and Shaping a Sustainable Future. Read more about working at Aalto: https://www.aalto.fi/en/careers-at-aalto Check out our new virtual campus experience: https://virtualtour.aalto.fi About Finland Finland is a great place for living with or without family – it is a safe, politically stable and well-organized Nordic society. Finland is consistently ranked high in quality of life and was listed again as the happiest country in the world: World Happiness Report 2025 For more information about living in Finland: Aalto Careers for International Staff. More about Aalto University: Aalto.fi youtube.com/user/aaltouniversity linkedin.com/school/aalto-university/ www.facebook.com/aaltouniversity instagram.com/aaltouniversity To view information about Workday Accessibility, please click here. Please see more of our Open Positions here.

About PSDI The Physical Science Data Infrastructure (PSDI) initiative is an EPSRC funded large scale infrastructure project between the University of Southampton and the Science and Technologies Facilities Council (STFC). PSDI aims to enable researchers in physical sciences to discover, capture, manage and share research data more easily by connecting and enhancing the different data infrastructures they use. About the role We are looking for someone with a background in the physical sciences, excellent communication skills and a passion for data and metadata to join our team to help to develop PSDI’s content in line with community best practices. You will partner with academic researchers in the physical sciences to guide them when they plan, collect and share their data outputs, and capture corresponding descriptive metadata. You will undertake these tasks when onboarding resources to PSDI, providing consultancy, training or delivering presentations. You will proactively progress these projects toward end goals, reviewing previous actions, addressing blockers and identifying next steps.  There is a vibrant wider community who are developing best practices in this area and implementing them in various ways – you will show a keen interest in working with them and keeping up to date with these developments. You must have a master’s degree, equivalent professional qualifications or experience in a physical sciences subject. You must have experience in handling, organising and managing physical sciences research data – either your own or that of others – and will have given careful consideration to how to maximise its usefulness for others. You must also have excellent technical presentation and communication skills, and experience of disseminating knowledge via presentations, training or teaching. Experience of technical consultancy or training, knowledge of physical sciences data and metadata standards and experience of physical sciences research practices and programmatic data manipulation and semantic technologies are all desirable skills. You will make a valuable contribution to PSDI’s goals, making contributions from research to user engagement and have the potential to grow your own areas of expertise. There will be opportunities to develop skills to advance your career in the flourishing area of research data management.  What we can offer you  The position is offered as a full-time post with a permanent contract, contingent upon ongoing funding of PSDI (which is currently guaranteed until 31st March 2027). The post is based at the University of Southampton in the Faculty of Engineering and Physical Sciences.  Equality, diversity and Inclusion is central to the ethos in Chemistry, having held an Athena SWAN Silver award since 2014. We particularly encourage women, Black, Asian and minority ethnic (BAME), LGBT and disabled applicants to apply for this position. Chemistry considers applicants that wish to work flexibly, and due consideration is given to applicants who have taken a career break. The University has a generous maternity policy and onsite childcare facilities. For further information please visit https://www.southampton.ac.uk/chemistry/about/Equality/index.page

Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics. We are seeking a Research Assistant to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model performance and robustness, and (ii) exemplary passion and motivation to pursue multidisciplinary research at the intersection of computing and healthcare. Methodologies of interest include: multi-modal learning, foundation models, including large language models, agentic AI, multi-agent AI systems, transfer learning, self-supervised learning, and federated learning. The Research Assistant will be primarily based at NYU Abu Dhabi. The researcher will report directly to Dr. Farah Shamout and work in close collaboration with other researchers, PhD students, and undergraduate research assistants. The researcher will engage with our regular collaborators across the NYU campuses and local medical institutions in the UAE. Key Responsibilities Research Support the supervisor in developing and implementing the research agenda; Conduct high-quality and innovative research primarily focused on ML for healthcare; Design and implement experiments to compare proposed work with SOTA baselines; Publish research findings in high-impact journals and conferences; Communicate and present research findings at international academic gatherings; Create, maintain, and document high-quality research code for reproducibility; Maintain good practice in managing and accessing sensitive medical datasets; And collaborate with scientists within the NYU Global Network and in Abu Dhabi. Minimum Qualifications Currently has or is in the process of completing a bachelor’s or master’s degree from a recognized institution Bachelor’s/ Master’s degree in computer science, mathematics, computer engineering, or relevant technical field Demonstrable research experience involving data pre-processing and preparation for machine learning models Demonstrable research experience in conducting experiments for training and evaluating deep neural networks Knowledge of multi-modal learning, transfer learning, transformers, or self-supervised learning Experience in dealing with large medical datasets (e.g., electronic health records data or medical images) Ability to use high performance computing cluster Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a team to get the work done Excellent communication skills (oral and written communication) Willingness to learn and confront new challenges Preferred Qualifications Bachelor’s/ Master’s thesis conducted in the area of machine learning for healthcare and related topics First-author peer-reviewed published papers (or under review) Evidence of leadership and service activities in the academic domain For consideration, applicants need to submit a cover letter, curriculum vitae with full publication list, research statement (1-page), project proposal summary (1-page), and three letters of reference, all in PDF format. If you have any questions, please email Prof. Farah Shamout at farah.shamout@nyu.edu. The terms of employment are very competitive and include housing and educational subsidies for children. Applications will be accepted immediately and candidates will be considered until the position is filled. Please visit our website at http://nyuad.nyu.edu/en/about/careers/faculty-positions.html for instructions and information on how to apply.  About NYU Abu Dhabi https://nyuad.nyu.edu/en/ NYU Abu Dhabi is the first comprehensive liberal arts and research campus in the Middle East to be operated abroad by a major American research university. Times Higher Education ranks NYU among the top 30 universities in the world, making NYU Abu Dhabi the highest-ranked university in the UAE and MENA region. NYU Abu Dhabi has integrated a highly selective undergraduate curriculum across the disciplines with a world center for advanced research and scholarship. The university enables its students in the sciences, engineering, social sciences, humanities, and arts to succeed in an increasingly interdependent world and advance cooperation and progress on humanity’s shared challenges. NYU Abu Dhabi’s high-achieving students have come from over 120 countries and speak over 100 languages. Together, NYU's campuses in New York, Abu Dhabi, and Shanghai form the backbone of a unique global university, giving faculty and students opportunities to experience varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents.  NYUAD is committed to upholding a culture of non-discrimination, anti-harassment, dignity, and mutual respect; providing equal access and opportunity; and fostering academic excellence in learning, research, and teaching. Students are drawn from among the world’s best. They are bright, intellectually passionate, and committed to building a campus environment anchored in mutual respect, understanding, and care. The NYUAD undergraduate student body has garnered an impressive record of scholarships, graduate-school admissions, and other global honors. Graduate education is an area of growth for the University; the current graduate student population of over 100 students is expected to expand in the next decade as doctoral programs are developed. Working for NYUAD At NYUAD, we recognize that Abu Dhabi is more than where you work; it’s your home. In order for research staff to thrive, we offer a comprehensive benefits package. This starts with a generous relocation allowance; educational assistance for your dependents; access to health and wellness services; and more. NYUAD is committed to research staff success throughout the academic trajectory, providing support for ambitious and world-class research projects and innovative, interactive teaching approaches. Support for dual-career families is a priority. Visit our website for more information on benefits for you and your dependents. NYUAD is an equal-opportunity employer. We welcome applications from all qualified candidates and seek individuals who will contribute to the excellence and vibrancy of our academic community. Applications are welcome from all qualified candidates. In line with UAE regulations, Emirati candidates are encouraged to apply. Join NYU Abu Dhabi, an exceptional place for exceptional people. NYUAD values belonging and respect; such principles are fundamental to the university’s commitment to excellence. NYUAD is an equal-opportunity employer. We welcome applications from all qualified candidates and seek individuals who will contribute to our vibrant, multidisciplinary research and teaching community. Multidisciplinary research and exceptional teaching in a global campus community are hallmarks of the University’s mission. @WorkAtNYUAD