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Autonomous University of Sinaloa

Ranked
sdgs/sdg overall
Sustainability Impact Rated
Espinoza de los Monteros, Mexico
1201–1500th in World University Rankings 2026
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About Autonomous University of Sinaloa

Basic information and contact details for Autonomous University of Sinaloa

institution

The Autonomous University of Sinaloa (UAS) is a public university founded in 1873 in Sinaloa, Mexico.

UAS has more than 40 undergraduate programmes, 40 master's and 12 PhD programmes including agronomy engineering, business and international trade, chemical and biological engineering, computer science, dentistry, education, gastronomy, English language teaching, surveying engineering, history, journalism, nutrition, optometry, public policy, social work and veterinary medicine and zootechnics to name but a few.

Located in northwest Mexico, the university has five campuses situated along northern, mid-northern, central and southern Sinaloa. With a population of 128,077 students, the university is considered to be the leading academic institution in regard to having the most encompassing coverage in the region.

The university is renowned for having quality academic conduct and governing systems, in addition to accreditations and certifications from agencies with national and international recognition including universities and institutions from South America, China, Russia, Spain and the USA.

UAS also has an entrepreneurship and innovation centre that has been created to strengthen the creative abilities and leadership skills of young entrepreneurs and support innovative business projects.

Famous for its band music and several thousand year-old game known as ulama, the state is filled with both cultural and historical attractions. Its position on the sea makes it popular among vacationers. Its most popularly visited city is Mazatlan, a coastal resort town known for its beaches of lustrous white sand and spectacular surfing. Santa Maria Island and its surrounding islands are also noted tourist destinations. Many people take up fishing while there and catch mahi-mahi, sailfish, and swordfish. There are also cultural and historical attractions throughout the state. Sinaloa’s capital, Culiacan Rosales, is also its largest city.

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Key Student Statistics

A breakdown of student statistics at Autonomous University of Sinaloa

gender ratio
Student gender ratio
55 F : 45 M (1)
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International student percentage
9% (1)
student per staff
Students per staff
17.3 (1)
student
Student total
79206 (1)

Based on data collected for the (1) World University Rankings 2026

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Professional Officer (All Levels) (Robotics & Automation)

SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)

Singapore Institute of Technology (SIT)

Singapore

institution

Singapore Institute of Technology (SIT)

Singapore


Job Purpose The Professional Officers Division (POD) houses a centralized pool of academic staff under the Professional Officers scheme. Professional Officers come with specialised and deep technical skills acquired through extensive industry experience. They form the talent pool which brings a much-needed industry perspective to student learning. They facilitate applied learning and applied research in SIT, complementing the academic expertise of the faculty to bring industry practices and applications into the curriculum. Professional Officers leverage on their industry experiences to create authentic learning environments, where discovery and innovation take place. They act as coaches and mentors to students during practical learning activities such as laboratory sessions, Capstone Projects and the Integrated Work Study Programme (IWSP). Professional Officers could also lead or work with faculty on industry innovation projects to provide solutions to the industry. In addition to their role in applied learning and applied research, Professional Officers manage the centralised laboratory facilities and resources in SIT. With Technical Officers, laboratory safety professionals, and administrators in POD, they jointly develop central policies and processes for the safe and seamless operation of laboratories in SIT. Key Responsibilities Design and teach labs & practice modules. Mentor students in Capstone Projects and the Integrated Work Study Programme (IWSP). Lead or co-lead innovation projects with industry. Manage labs and equipment to support academic programmes and applied research. Ensure safety in labs. Job Requirements A good degree in Electronics, Mechatronics, Software Engineering, or a related field. Masters, PhD and/or relevant professional certifications would be advantageous. Deep technical specialist with 10 or more years of working experience in Automation, Robotics, Autonomous Systems, or related areas in development would be preferred. Experience in following areas is highly desirable: Hands-on experience in electronics/electrical. Programming of Microcontrollers and embedded systems. Working knowledge of programming languages such ROS/ROS2, Python, C/C++ Programming, Knowledge of subjects such as computer vision, Machine Learning, Artificial Intelligence and Kinematics and dynamics. Autonomous systems, Robotics and Automation. Industry 4.0 and Internet-of-Things. Advantageous to have prior knowledge in: Hands-on experience implementation of industrial robotics systems and performing offline simulation of robotic work cells. Digitalisation skills – IoT, data analytics, artificial intelligence, machine learning, mixed reality, etc would be advantageous. Programming and simulation software such as MATLAB, Gazebo and Tensor flow. Knowledge of Linux OS. Extensive hands-on experience and knowledge of industry practices and engineering principles to bring industry perspective to SIT. Keen interest in innovation projects, with demonstrated ability in developing solutions to technical problems. Strong supervisory skills and enjoy working closely with students in an educational environment. Demonstrate proficiency to keep abreast of development in the field and pursue professional certification programs. Possess industrial certifications in relevant areas will be an added advantage.

Salary

Competitive

Posted

26 Dec 2025

Post-Doctoral Associate in the Division of Engineering - Dr. Farah Shamout

NEW YORK UNIVERSITY ABU DHABI

New York University Abu Dhabi Corporation

United Arab Emirates, Abu Dhabi

institution

New York University Abu Dhabi Corporation

United Arab Emirates, Abu Dhabi


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 Postdoctoral Researcher 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 Federated learning The Postdoctoral Researcher 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 methodology development for healthcare; Generate new high-impact ideas based on gaps and limitations of the state-of-the-art (SOTA); 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; Assist the supervisor in the preparation of grant applications (as appropriate); And collaborate with scientists within the NYU Global Network and in Abu Dhabi. Training & professional development Attend trainings and workshops for career development; Mentor PhD students and undergraduate research assistants (as appropriate); Actively participate in events and committees at NYU Abu Dhabi, such as the Postdoctoral Council Steering Committee; Gain experience in applying for local research grants (subject to eligibility); And transition to independence to pursue a career of choosing following the appointment. * The researcher will create a personalized training and development plan with the supervisor. Minimum Qualifications Currently has or is in the process of completing a PhD, MD/PhD, DPhil or equivalent terminal degree from a recognized institution (no more than 5 years since completing the doctoral degree) Doctoral research in the area of machine learning and artificial intelligence Bachelor’s/ Master’s degree in computer science, mathematics, computer engineering, or relevant technical field First-author peer-reviewed published papers (or under review) Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) with several years of practice 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 Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and 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 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, and a transcript, 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 NYUAD: NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities. EOE/AA/Minorities/Females/Vet/Disabled/SexualOrientation/Gender Identity Employer UAE Nationals are encouraged to apply.

Salary

Competitive

Posted

26 Dec 2025

Professor and Director, Pan Sutong SH-HK Economic Policy Research Institute

LINGNAN UNIVERSITY

Lingnan University

Hong Kong, Tuen Mun

institution

Lingnan University

Hong Kong, Tuen Mun


Lingnan University is one of the eight publicly funded institutions in the Hong Kong Special Administrative Region (HKSAR) and has the longest established tradition among the local institutions of higher education. It is widely recognised for providing quality education with a focus on whole-person development and conducting high-impact research for a better world. Moving forward, Lingnan University aspires to become a comprehensive university in arts and sciences in the digital era, with impactful research and innovations. Lingnan University offers undergraduate, taught postgraduate, and research postgraduate programmes in the Faculties of Arts, Business, Social Sciences, and the Schools of Data Science, Graduate Studies and Interdisciplinary Studies. To foster interdisciplinary collaboration and scientific progress, Lingnan University established the Lingnan University Institute for Advanced Study (LUIAS), attracting distinguished scholars from around the world to collaborate with its faculty and students. With traditional strengths in arts, business, social sciences, and interdisciplinary studies, the University aims to equip students with practical knowledge and critical thinking skills to thrive in the future. Subsequent to the establishment of the School of Data Science and LUIAS, Lingnan University is transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/. Applications are now invited for the following post: Professor and Director Pan Sutong Shanghai-Hong Kong Economic Policy Research Institute (Post Ref.: 24/245) Pan Sutong Shanghai-Hong Kong Economic Policy Research Institute (PSEI) aims to promote policy-related research on economic development of the Mainland and Hong Kong. It will synergise resources and expertise in the Mainland and Hong Kong to strengthen research on salient issues related to the economic and financial development of the two places. It will also provide research consultancy on industry and economic policies, in addition to offering policy advice to the authorities to foster mutual prosperity of the Mainland and Hong Kong. The Institute is seeking an outstanding scholar with distinguished scholarly accomplishment in research, teaching and knowledge exchange in the field of policy-related research on economic development of the Mainland and Hong Kong. The appointee will be appointed as a Professor in the Department of Economics and concurrently as Director of the PSEI. The Director of PSEI will be expected to (i) provide strategic direction and leadership for the operation of the Institute to ensure research quality and effectiveness; (ii) formulate and implement strategies for networking and building rapport with academia, business sector, industries and other related stakeholders; (iii) explore new collaboration opportunities with community partners and stakeholders and (iv) identify and secure funding opportunities through grants, partnerships and sponsorships to support the research projects/programmes and various activities of the Institute. General Requirements Applicants should have a PhD degree in Economics or relevant discipline, with substantial teaching experience and excellent track record in research and scholarly activities. Good networking capability and ability to establish linkages with the business sector, industries and other research stakeholders would be a definite advantage. Salary and Benefits The conditions of appointment will be competitive. The remuneration will be commensurate with qualifications and experience. Fringe benefits include annual leave, medical and dental benefits, mandatory provident fund, gratuity and incoming passage and baggage allowance for the eligible appointee. Appointment will normally be made on an initial contract of three years, which, subject to review and mutual agreement, may lead to longer-term appointments with possibility of consideration for substantiation. For applicants with outstanding credentials, substantiation at appointment will be considered. Application Procedure (online applications only) Please click "Apply Now" to submit your application. Applicants shall provide names and contact information of at least three referees to whom applicants’ consent has been given for their providing references. Personal data collected will be used for recruitment purposes only. We are an equal opportunities employer. Review of applications will continue until the post is filled. Qualified candidates are advised to submit their applications early for consideration. The University reserves the right not to make an appointment for the post advertised, or to fill the post by invitation or by search. We regret that only shortlisted candidates will be notified.

Salary

Competitive

Posted

26 Dec 2025

Postdoctoral Fellow - Frontier of AI driven Inverse Design of Functional Materials, WJYSIS

LINGNAN UNIVERSITY

Lingnan University

Hong Kong, Tuen Mun

institution

Lingnan University

Hong Kong, Tuen Mun


Lingnan University is one of the eight publicly funded institutions in the Hong Kong Special Administrative Region (HKSAR) of the People’s Republic of China (PRC) and has the longest established tradition among the local institutions of higher education. It is widely recognised for providing quality education with a focus on whole-person development and conducting high-impact research for a better world. Moving forward, Lingnan University is well positioned to take lead as a comprehensive university in arts and sciences in the digital era, with impactful research and innovations. Lingnan University offers undergraduate, taught postgraduate, and research postgraduate programmes in the Faculties of Arts, Business, Social Sciences, and the Schools of Data Science, Graduate Studies and Interdisciplinary Studies. To foster interdisciplinary collaboration and scientific progress, Lingnan University established the Lingnan University Institute for Advanced Study (LUIAS), attracting distinguished scholars from around the world to collaborate with its faculty and students. With traditional strengths in arts, business, social sciences, and interdisciplinary studies, the University aims to equip students with practical knowledge and critical thinking skills to thrive in the future. Subsequent to the establishment of the School of Data Science and LUIAS, Lingnan University is transforming into a hub for global leaders to develop and promote human-centric technology and social policies. Further information about Lingnan University is available at https://www.ln.edu.hk/. Applications are now invited for the following post: Postdoctoral Fellow - Frontier of AI driven Inverse Design of Functional Materials Wu Jieh Yee School of Interdisciplinary Studies (Post Ref.: 25/176) Professor Xi Chen, Dean of the Wu Jieh Yee School of Interdisciplinary Studies and Chair Professor of Interdisciplinary Studies, leads an interdisciplinary team working at the frontier of AI driven inverse design of functional materials. Current research directions include: Reversible material representation methods for accelerated inverse design Large language, diffusion & graph neural models for materials discovery Fine tuning and architecture optimisation of foundation models Inverse design of next generation functional materials (e.g. energy, sustainability) The group partners closely with leading institutes in Hong Kong, Shenzhen, and worldwide, offering an exciting, collaborative research environment. The appointee will work as part of the team. This involves (1) conducting independent and collaborative research leading to publications in top tier journals and conferences; (2) contributing to grant proposal writing and collaborative projects; and (3) mentoring junior researchers and assisting with laboratory management. Requirements The candidate should have obtained / been conferred a PhD degree or an equivalent qualification in Materials Science, Mechanical / Applied Mechanics, Physics, Computer Science, Data Science, or a closely related field for no more than three years prior to the commencement of the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential. Additionally, the candidate should possess an excellent command of written and spoken English and demonstrate the ability to work independently as well as part of a multidisciplinary team. Appointment The conditions of appointment will be competitive. Commencing salary will be commensurate with qualifications and experience. Fringe benefits include annual leave, medical and dental benefits, mandatory provident fund, gratuity, and incoming passage and baggage allowance for the eligible appointee. The appointment will normally be made on an initial contract of up to two years. Application Procedure (online application only) Please click "Apply Now" to submit your application. Personal data collected will be used for recruitment purposes only. We are an equal opportunities employer. The review of applications will start and continue until the post is filled. Qualified candidates are advised to submit their applications early for consideration. The University reserves the right not to make an appointment for the post advertised, or to fill the post by invitation or by search. We regret that only shortlisted candidates will be notified.

Salary

Competitive

Posted

26 Dec 2025

University Assistant Predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning

UNIVERSITY OF VIENNA

University of Vienna

Austria, Vienna Danubepier Hov

institution

University of Vienna

Austria, Vienna Danubepier Hov


At the University of Vienna more than 10,000 personalities work together towards answering the big questions of the future. Around 7,500 of them do research and teaching, around 2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral/PhD Candidate Optical Quantum Computing and Machine Learning 51 Faculty of Physics Startdate: 01.02.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.01.2030 Reference no.: 5037 We invite you to join the Operational Quantum Information Team (Dakić Group) to work on topics in quantum information theory and quantum technologies, with a special focus on optical quantum computing and machine learning with nonlinear mechanical systems. Your personal sphere of influence: As a university assistant (praedoc) in this 4-year position, you will be part of the Operational Quantum Information Team around Professor Borivoje Dakić. Our team is part of the Quantum Optics, Quantum Nanophysics, and Quantum Information group of the Faculty of Physics. We are a member of the Vienna Center for Quantum Science and Technology (VCQ), one of the largest quantum hubs in Europe, and of the Austrian Cluster of Excellence (quantA), advancing basic research in quantum sciences, aiming to expand the frontiers of knowledge and thus being the engine for future innovations. You will also benefit from being a fellow of the Vienna Doctoral School in Physics (VDSP), being part of a thriving community with more than 100 quantum scientists on premises and about 300 quantum researchers in Vienna. The Dakić group is an international team of young scientists (Master / PhD / Postdoc). Our expertise lies in quantum foundations, quantum information theory and quantum technologies. For additional information, please visit: https://dakic.univie.ac.at/. Your future tasks: You will actively participate in research, teaching and administration. This means: You are involved in a well-funded research project on the advertised research topics. You will present your research plan to the faculty and complete a dissertation agreement within 12–18 months. This will be reviewed and adapted on an annual basis. You will work on your dissertation and towards its completion in time. We expect a large degree of independence paired with a high level of social awareness, as the goal will only be achieved in a team. You will contribute to teaching (lab courses, exercise classes) within the provisions of the collective bargaining agreement. You will fulfil some administrative tasks, contributing to the success and self-organization of the group in research, teaching and administration. You will continuously stay informed about the state of the art in your field. You will contribute to outreach by publications, conference presentations and public activities. This is part of your personality: We expect a strong candidate with the following requirements: You have completed your Master’s degree or Diploma in Physics, Mathematics, Computer Science or a closely related field, ideally with a strong focus on quantum computing and numerical simulations. Experience in data-reuploading quantum algorithms, optical quantum computing and quantum machine learning (highly desirable). Experience in machine-learning schemes based on arrays of nonlinearly interacting mechanical oscillators (highly desirable). Experience in programming skills (e.g. Python, Qiskit, MATLAB, C, numerical simulations). You have an excellent command of written and spoken English. What we offer: Inspiring working atmosphere: You are part of an international academic team in a healthy and fair working environment. Good public transport connections: Your workplace in the center of beautiful Vienna is easily accessible by public transport. Potential for development: Success in life depends on what you make of it, but if you are ambitious and successful, there are plenty of opportunities to connect you to all relevant top research groups in the world in quantum information, quantum technologies and machine learning. Internal further training & coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses to choose from – free of charge. Fair salary: The basic salary of EUR 3.714,80 (full-time basis, 14x p.a.) increases if we can credit professional experience. The employment duration is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration. Equal opportunities for everyone: We look forward to diverse personalities in the team! It is that easy to apply: Please send us: Your scientific curriculum vitae. A statement on your research interests for the future (motivation letter), with particular emphasis on your experience in optical quantum computing and data-reuploading algorithms, and machine-learning schemes based on arrays of nonlinearly interacting mechanical oscillators. Contact information of two referees. Your Bachelor’s and Master‘s degree certificates and transcripts. If you have any content questions, please contact: Borivoje Dakic borivoje.dakic@univie.ac.at We look forward to new personalities in our team! The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity. We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 01/06/2026 (MM-DD-YYYY) Prae Doc

Salary

The basic salary of EUR 3.714,80 (full-time basis, 14x p.a.)

Posted

26 Dec 2025

Subjects Taught at Autonomous University of Sinaloa

See below for a range of subjects taught at Autonomous University of Sinaloa

Arts and Humanities

  • Architecture
  • Art, Performing Art and Design
  • History, Philosophy and Theology
  • Languages, Literature and Linguistics

Business and Economics

  • Accounting and Finance
  • Business and Management
  • Economics and Econometrics

Computer Science

  • Computer Science

Education Studies

  • Education

Engineering

  • Chemical Engineering
  • Civil Engineering
  • Electrical and Electronic Engineering
  • General Engineering
  • Mechanical and Aerospace Engineering

Law

  • Law

Life Sciences

  • Agriculture and Forestry
  • Biological Sciences
  • Sport Science
  • Veterinary Science

Medical and Health

  • Medicine and Dentistry
  • Other Health

Physical Sciences

  • Chemistry
  • Geology, Environmental, Earth and Marine Sciences
  • Mathematics and Statistics
  • Physics and Astronomy

Psychology

  • Psychology

Social Sciences

  • Communication and Media Studies
  • Geography
  • Politics and International Studies
  • Sociology