California State University, Northridge

Description The Modern Compilers Lab in the Computer Science program at New York University Abu Dhabi, seeks to recruit a research assistant to work on the intersection of compilers and deep learning. Many companies, such as Google, Facebook, and Amazon are building new specialized programming frameworks. This is because these companies need to allow their users to write simple, high-level code and run it efficiently on different hardware architectures. For example, Google has built TensorFlow, a framework for deep learning allowing users to run deep learning on multiple hardware architectures without changing the code. Our research team at NYUAD (New York University Abu Dhabi) is developing a new programming framework called Tiramisu [1]. Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines). In order to have the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel), enhancing data locality by fusion, and blocking (i.e. accessing arrays in a way that improves temporal and spatial data locality). A large number of optimizations exist and choosing which optimization should be used and which should not is important for performance. In some situations, some optimizations are harmful to performance while they are beneficial in other situations. Currently, there is no way to help users choose which optimizations should be used. Expert programmers usually spend a lot of time trying different optimizations manually to find the best set of optimizations. The goal of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module that relies on machine learning has been developed and we want to take that module to the next level. The final product of this project would be a compiler pass that allows Tiramisu to automatically choose which optimization should be used for a given unoptimized program. We want to produce a high-quality technique that can be used by the users of Tiramisu and especially by our partner companies and research labs. Candidates must hold a bachelor’s degree or equivalent in Computer Science, though a master’s degree is preferred. The ideal candidate will have Internship or experience in the areas of compilers and artificial intelligence. For consideration, applicants need to submit a cover letter, curriculum vitae, transcript, statement of research interests and two letters of reference, all in PDF format. If you have any questions, please email Prof. Riyadh Baghdadi at baghdadi@nyu.edu or mrasras@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. 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. References [1] http://tiramisu-compiler.org/

The Machine Learning for Health team at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our team. Project overview Healthcare systems collect vast amounts of heterogeneous data routinely through interactions with individuals. Our mission is to use machine learning and data science to find better ways to utilize this data in healthcare planning, personalized medicine and targeted recall-studies. In this position, you will be a part of an expanding team working on difficult and meaningful challenges together with clinicians. You get to work with multimodal nationwide Finnish health data from 7 million individuals including for example medication purchases, laboratory measurements, clinical notes and imaging. Your main project will involve building and studying multimodal health trajectories on a nationwide scale to catch increasing disease risk as early as possible. In addition, this data, and similar datasets from our collaborators across the Nordics, Baltics and Europe present a fantastic opportunity to pursue your own ambitious research goals. Our other research directions of interest include privacy of AI models trained on health data as well as training AI models on health data across national borders. Key responsibilities Design, implement and benchmark machine learning models for large-scale health datasets consisting of diverse information including structured medical history, demographics, clinical notes, laboratory measurements, omics and genetics. Collaborate closely with other team members, and national/ international collaborators. Assist other team members in experiment planning, data processing and interpretation of results. Contribute to writing scientific articles, conference presentations, grant applications and supervision of Master’s students and PhD researchers. Take part in open science and code sharing. Skills, expertise and qualifications A PhD degree in computational biology, machine learning, computer science, data science, bioinformatics, or a related discipline Demonstrated machine learning experience in form of high-quality scientific publications, experience working with real-world health data is a plus Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch, and familiarity with multimodal data fusion is highly desirable Ability to work independently, as well as collaboratively in interdisciplinary and international research teams Willingness to supervise junior researchers and develop your supervision skills Excellent command of spoken and written English Research group The Machine Learning for Health team focuses on applying artificial intelligence to derive meaningful insights from large, real-world health and biomedical datasets. The team is led by Dr. Tuomo Hartonen. We have active collaborations with leading researchers in the EU, UK and US, with opportunities for international research visits during the employment. We collaborate closely with several research groups at FIMM and at ELLIS Institute Finland. We work with diverse international datasets ranging from Finnish and French national health registries to biobank studies such as FinnGen (Finland), UK Biobank (UK) and All of Us (United States). Contract and salary The position is initially offered for two years, and can be possibly extended. A trial period of six months will be applied. The starting salary will be ca. 3900-4100 euros/month, depending on the appointees’ qualifications and experience, and is based on the University of Helsinki salary system. The University of Helsinki offers comprehensive services to its employees, including occupational health care, support in access to high quality sports facilities, and vast opportunities for professional development. Standard Finnish pension benefits are provided for university employees. Apply at latest by August 16th, 2026. The call may be extended until a hiring decision is made. How to apply: The application should include: A one-page letter stating the motivation for applying to this position. A maximum two-page CV. List of scientific publications. An official copy of the PhD degree certificate. Contact information of 2 references (name, relation to candidate, e-mail and telephone number) who have agreed to provide a written statement on behalf of the applicant. Note that the references will be contacted only at a later stage of the process. To apply, please submit the application through the University of Helsinki electronic recruitment system by clicking on Apply for job. Internal applicants (i.e., current employees of the University of Helsinki) please submit your applications through SAP Fiori https://msap.sap.helsinki.fi. For further information on the position, please contact Dr. Tuomo Hartonen (tuomo.hartonen@helsinki.fi).  A diverse and equitable study and work culture is important to us. That is why we do our best to promote an inclusive university community. We encourage all qualified applicants from diverse backgrounds to apply for our positions. Click this link to read about accessibility and inclusivity at our University. JOIN US TO BUILD A BETTER WORLD – TOGETHER! #HelsinkiUniCareers The Institute for Molecular Medicine Finland (FIMM) is an international research unit focusing on human genomics and personalized medicine at the Helsinki Institute of Life Science (HiLIFE) of the University of Helsinki - a leading Nordic university with a strong commitment to life science research. As part of Academic Medical Center Helsinki in Meilahti campus FIMM collaborates locally with the Faculty of Medicine, Helsinki University Hospital and National Institute for Health and Welfare. FIMM is part of the Nordic EMBL Partnership for Molecular Medicine, composed of the European Molecular Biology Laboratory (EMBL) and the centres for molecular medicine in Norway, Sweden and Denmark, and the EU-LIFE Community.

Job Responsibilities The successful candidate will be expected to: Teach and assess undergraduate courses related to business administration (e.g. Introduction to Business Adminstration, Academic Writing). Develop course materials, teaching plans, assignments, and assessment methods aligned with international academic standards and applied learning principles. Supervise student projects, group work, workshops, and practical training activities. Contribute to curriculum development and continuous improvement of teaching quality. Conduct independent and collaborative research.   Job Qualifications Doctor’s degree in business with a distinguished teaching record, highly committed to teaching excellence. Strong commitment to research with a proven track record. Experience in supervising undergraduate projects, research reports, or student competitions. Excellent communication skills in English; Chinese language skills are a plus Industry experience preferred   Expected Onboarding Date August 2026 Salary and Benefits Competitive Salary in the Market (13th Month Salary) Allowance: Travel Allowance / Phone Allowance / High Temperature Allowance, Etc. 5 Types of Social Insurance and 1 Housing Provident Fund Commercial insurance Transitional Apartments Arrangement Working environment that is open to the world and strongly oriented towards sustainability, diversity and internationality Paid Holidays Statutory Holidays (13days) Annual Leave (25 days) Paid Sick Leave Paid Marriage Leave Paid Maternity Leave Paid Paternity Leave Parental Leave How to apply Please prepare the following documents: Current English CV(including full publication list) Cover Letter(addressed to the President) 2 Reference Letters Teaching Qualification Documents, Qualifications (if available) Documents certifying the professor’s (if available) Academic Degree Certificates Please import all files into one PDF Please name the PDF as ” Position – Your Name” Please send all files to recruitment@hibiuh.edu.cn