Pompeu Fabra University

Description The Division of Science at New York University Abu Dhabi is inviting applications for Postdoctoral Associate positions in the broad research areas of mathematical analysis and partial differential equations (PDEs). While all applicants with a background in the analysis of PDEs will be considered, candidates with prior experience in theoretical physics, fluid mechanics, kinetic theory, dispersive equations or harmonic analysis will be given special attention. The Postdoctoral Associates will be appointed for a duration of two years and will work under the supervision of Associate Professor Diogo Arsénio on subjects including: Magnetohydrodynamics and the mathematical theory of plasmas. Collisional kinetic models and their hydrodynamic limits. Dispersive phenomena in kinetic theory. Quantum gases. Applicants must hold a PhD degree in mathematics, physics or a related field. We seek candidates with an outstanding academic record. The terms of employment are internationally competitive, and they include housing subsidies, as well as educational support for families. Prospective candidates for the position must submit the following items online through the link in PDF format: Cover letter Curriculum vitae including a list of publications. Transcript of PhD degree or, in case the degree has not yet been formally awarded, evidence of completion of the degree requirements and expected conferral date. A brief research statement detailing the applicant’s scientific interests, accomplishments and future research strategies. At least two letters of recommendation to be sent confidentially (applicants will be prompted to enter the referee’s e-mails) The screening and assessment of applications will begin immediately and will continue until positions are successfully filled. NYUAD is an equal opportunity employer committed to equity, diversity, and social inclusion. This position is not in the United States. The applicant must be willing to relocate to Abu Dhabi, United Arab Emirates. 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. UAE Nationals are encouraged to apply.

Description The Prof. Sunil Kumar’s Research Group in the Division of Engineering, New York University Abu Dhabi, seeks to recruit a research assistant to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines and implement multimodal retrieval with re-rankers for robust profile selection. Design and train advanced AI models for digital twin: 3D model learning, prediction models from imaging and molecular data, model-based simulation coupling, and uncertainty-aware outputs for lab/clinical validation. Work with 3D datasets, time-series sensors, and multi-omics or assay data to create predictive models and digital twin prototypes. Maintain reproducible code, experiments, and model/ dataset versioning; develop deployment artifacts (Docker, CI/CD scripts) and support cloud/HPC model training. Plan, run and analyze pilot studies (SME pilots and lab validation); participate in data collection, annotation and lab assay coordination. Write up results and contribute to publications, technical reports and presentations for academic and non-academic stakeholders. Minimum Qualifications: Bachelor’s or Master’s degree in computer science, electrical/computer engineering, applied mathematics, computational biology, bioengineering, or a closely related discipline. Solid experience training and evaluating AI models. Proficiency in Python and ML frameworks (PyTorch, TensorFlow). Experience with dataset curation, annotation workflows, FAISS/embedding retrieval, LLM-based parsing, RAG-style pipeline, and GPU/HPC training. Familiarity with 3D data processing or willingness to learn quickly. Publications, thesis work, or demonstrable projects in computer vision, multi-modal ML, digital twins or biomedical ML. Familiarity with uncertainty quantification and model explainability methods. Strong software engineering practices: Git, reproducible experiments, modular code, deploying models in cloud (AWS/GCP) and containerized services (Docker, Kubernetes). Good written and verbal communication skills and ability to work collaboratively across interdisciplinary teams. For consideration, applicants need to submit a cover letter, curriculum vitae with full publication list statement of research interests, transcript and three letters of reference, all in PDF format. If you have any questions, please email Prof. Prabodh Panindre at prabodh@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 https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research.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. UAE Nationals are encouraged to apply.

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.  The primary responsibility of this role is to deliver on an industry funded research project where you will be part of the research team to develop advanced Nuclear Quadrupole Resonance (NQR) scanning technologies. Job Responsibilities Participate in research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met.  Undertake these responsibilities in the project:  i. Conduct comprehensive literature review on NQR technology, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system architecture, component selection, and experimental setup. iii. Conduct lab and site tests to evaluate the performance of NQR prototype. iv. Generate design documents and test documents at every stage of development until the final prototype. Coordinate procurement and liaison with vendors/suppliers. Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations.  Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements Has a bachelor’s degree in Electronics; Specialization in RF/Microwave or Digital Signal Processing (DSP) is preferred. Has solid knowledge of electronics, including circuit design, PCB layout, and hardware troubleshooting. Has relevant competence in the areas of Radio Frequency design and measurement; Familiarity with high-power amplifier systems is a plus. Has relevant experience in the areas of Digital Signal Processing (DSP) design and implementation is a plus. Experience in electromagnetic simulation using CST Studio Suite and circuit simulation with PSpice, LTspice, or equivalent is an advantage. Experience with developing software for instrument control and data acquisition using LabVIEW is helpful. Key Competencies Strong experimental skills and theoretical knowledge in electronic circuits, and RF or DSP design, with the ability to apply hands-on prototyping and system development. Able to build and maintain strong working relationships with people within and external to the university. Self-directed learner who believes in continuous learning and development Proficient in technical writing and presentation  Possess strong analytical and critical thinking skills Show strong initiative and take ownership of work